Explaining real exchange rate behaviour through tariffs ...hrmars.com/admin/pics/694.pdfequilibrium...

International Journal of Academic Research in Economics and Management Sciences April 2012, Vol. 1, No. 2

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Explaining real exchange rate behaviour through tariffs: the BRIC’s case

Leonardo TARIFFI

Universitat de Barcelona;

IESE Business School

[email protected]

ABSTRACT This paper aims to explain the importance of real variables and tariffs in the

structure of the behavior of real exchange rate in four emerging countries:

Brazil, Russia, India and China. Demonstrating the relevance of the Balassa-

Samuelson effect in the foreign exchange markets of the so-called BRIC

countries, this paper estimates both the long-run relationship, through

classical lineal stationary and cointegration techniques, and short-term

misalignment using an ordinary least squares method with error correction

mechanism.

KEY WORDS real exchange rate, tariffs, purchasing power parity, exchange rate misalignment, currency overvaluation, co-integration, unit roots test, error correction models, the Ricardo - Samuelson - Balassa effect

JEL CODES F31, F41, C22, C32

1. Introduction

Continuing fluctuations in the United States dollar exchange rate have increased the importance of analyzing the evolution of the currency exchange market. In both the academic and managerial sectors, the study of the exchange between different types of currencies is important because of its relationship with the balance of payments crisis and its link with competitiveness and growth of countries.

Likewise, international transactions have been modified by the increasing participation of emerging countries, not only in terms of real economy and world production, but also with regard to monetary and foreign exchange markets.

The so-called BRIC countries (Brazil, Russia, India and China) are examples of trade and international financial reorganization. These economies’ sustained economic growth is reflected in currency markets. In Figure 1, it can be observed that BRIC’s time series of real exchange rate (RER) has changed over time. To explain whether these changes are transitory or permanent is the first objective of this work.


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Having identified if there are permanent or transitory changes in the time series of the exchange rate, it can be found the reasons underlying these variations. These permanent changes may be linked to the real economy through structural mechanisms.

Figure 1: Real Exchage Rate

BRIC: Brazil, Russia, India and China

0.00000

2.00000

4.00000

6.00000

8.00000

10.00000

12.00000

14.00000

Q1

1995

Q4

1995

Q3

1996

Q2

1997

Q1

1998

Q4

1998

Q3

1999

Q2

2000

Q1

2001

Q4

2001

Q3

2002

Q2

2003

Q1

2004

Q4

2004

Q3

2005

Q2

2006

Q1

2007

Q4

2007

Q3

2008

Source: IFS/IM F - BLS - BDB - ROSSTAT - Reuters

0.000000

10.000000

20.000000

30.000000

40.000000

50.000000

60.000000

70.000000

Brazil China Russia India

The main reason to find real exchange rate determinants is that general macroeconomic time series, such as terms of trade, government expenditure, net foreign assets, trade balance, productivity or tariffs cause permanent changes in exchange rates and affect monetary and capital balances.

Moreover, there is a widely accepted consensus that trade and international transactions have been favored by the reduction in import tariffs. International negotiations have had a positive influence on tariffs reductions in products of most countries participating in the General Agreement on Tariffs and Trade (GATT). From the Dillon Round to the Uruguay Round, different mechanisms and formulas to change the level of tariffs consecutively have been proposed. Although the last meetings in the Doha Round did not had the expected success in achieving the stated objectives of trade liberalization, the previous agreements and bilateral negotiations between countries with different markets, growth rates and degree of trade protection have resulted in a decrease in tariffs.

From the World Trade Organization’s (WTO) foundation until 2008, BRIC countries have reduced their simple average of applied tariffs under the most favored nation principle by 43.68%, 19.44%; 66.39% and 60.15%, respectively. This decrease in import tariffs could affect the real exchange rate in the same way that it would affect a change in the aforementioned real variables (terms of trade, government expenditure, net foreign assets and trade balance) or an improvement in technological innovations.

To show the effects of real determinants and tariffs on the real exchange rate equilibrium is the second aim of this work.

This paper uses an empirical model to estimate real exchange rate in equilibrium (RERe) of the United States dollar with respect to other currencies belonging to BRIC countries: the real, ruble, the rupee and the yuan. It estimates the short-term relationship between the real exchange rate, real macroeconomic variables and tariffs to assess if they are statistically significant. Cointegration techniques are applied to find the long-term relationship, while the method of ordinary least squares (OLS) with error correction mechanism (ECM) is used to determine the short-term relationship.

If changes in exchange rates of countries belonging to BRIC (see Figure 1) are permanent and explained mainly by structural variables, the Balassa - Samuelson effect would be justified in this theoretical framework. The main target of this work is to find the real determinants in both, in both the short and


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long term, taking into account the Balassa - Samuelson effect and including tariffs as one of the explanatory variables.

Finally, the exchange rates overvaluation is calculated to indicate whether there are economic policies capable of compensating the underlying misalignments. This analysis is the ultimate goal outlined in this investigation. Through discretionary variations of real determinants, in particular reductions in import tariffs, policy makers can offset possible overvaluation.

This literature has been widely debated. Cassel introduced the concept of Purchasing Power Parity (PPP) in 1918. Dornbusch (1976) analysed capital mobility in the Mundell (1964) and Fleming (1962) model within an open economy to specify exchange rate determinants in the short term and international transmissions of monetary disturbances in the long run. Edwards (b1988), based on Balassa (1964) and Samuelson (1964), found other real determinants of the RER, and redefineds the theory of the international economy equilibrium. In 1996, Obstfeld and Rogoff formalized the relationship between RER and productivity through a dynamic partial equilibrium model which explains the Balassa - Samuelson effect.

On the other hand, the United Nations Conference on Trade and Development (UNCTAD) and the WTO provide information on consecutive decreases in tariff rates in their analysis and reports. Millet (2001) and Messerlin (2006) analyzed changes in tariffs and their relation with international negotiation rounds.

The empirical evidence related to this paper, such as that written by Frenkel (1981), Meese and Rogoff (1988), Froot and Rogoff (1994) and Clarida and Galí (1994) shows how relevant monetary variables are to explain exchange rate policies, but it is not conclusive about the levels or the signs of these changes affecting RER behavior. Edwards (a1988) includes the tariff rate as a determinant of the real exchange rate in his research but this influence is exerted through monetary mechanisms and not through the Balassa - Samuelson effect.

The following chapters present an examination of the theoretical framework and the RER definition. Secondly, after the presention of some facts about tariffs, the implemented methodology contrasting the empirical evidence is specified. Finally, the results and conclusions are presented.

2. Theoretical Framework

There are two main approaches which explain RERe. The first approach is based on the Purchasing Power Parity (PPP) theory studied by Cassel (1918) and the Mundell-Fleming model analysed by Dornbusch (1976). This theory extends the IS-LM model, with free capital mobility and flexible prices in a flexible exchange rate framework. Under these monetary assumptions, changes in the nominal exchange rate are diminished by the domestic - foreign price relationship in between countries. Monetary variations change prices. However, the nominal exchange rate reflects and opposes international price changes in a way that ensures that the PPP or the RER is constant over time.

In the long-term , the Mundell - Fleming model, with flexible prices in a flexible exchange rate framework, predicts that monetary expansion increases money supply, prices and nominal exchange rates, but it does not affect real variables such that RER. Nominal exchange rate depreciation keeps the purchasing power of domestic goods, with respect to foreign goods, in between the initial and the final equilibrium points. This fact implies international price level equivalence, when it is measured as a


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function of only one currency, value of money international equivalence, equilibrium stability in the PPP, neutrality of the long run RERe changes and the money causality function.

Assuming perfect capital mobility and exchange rate overreaction, the balance of payments is in equilibrium (BP=0) when the domestic interest rate is equal to the foreign interest rate (i=i*) (Tariffi, 2010). Domestic or foreign price changes are compensated by nominal exchange rate variations, so that RER is constant in the long run. The model is the following:

RER = c1 * [(NER * PE) / PD] (i)

Where NER is the nominal exchange rate, PE is external price for goods and services and PD is the domestic price for goods and services. Assuming PPP:

NER = c2 * (PD / PE) (ii)

Substituting (ii) in (i), obtains:

RERe = c1 * c2 = c3 (iii)

Any condition misalignment (iii) is temporary and is associated with transitory and speculative deviations. The term c3 could be defined as equal to zero, equal to a constant which is different from zero or equal to a constant plus a trend.

On the other hand, a second approach supported by Edwards (1988) shows that the RERe behaviour cannot be solely explained by monetary variables, but also by real variables. RER changes are not transitory. Fundamental real determinants cause permanent misalignments in the RERe. “The equilibrium real exchange rate is thatrelative price of tradables to non-tradables that, for given sustainable (equilibrium) values of other relevant variables such as taxes, international prices and technology, results in the simultaneous attainment of internal and external equilibrium. Internal equilibrium means that the non-tradable goods market clears in the current period, and is expected to be in equilibrium in future periods. In this definition of equilibrium RER, it is implicit the idea that this equilibrium takes place with unemployment at the “natural” level. External equilibrium, on the other hand, is attained when the intertemporal budget constraint that states that the discounted sum of a country’s current account has to be equal to zero, is satisfied. In other words, external equilibrium means that the current account balances (current and future) are compatible with long run sustainable capital flows” (Edwards 1988). This effect has been mathematically formulated by Maurice and Rogoff (c1996) through a partial equilibrium dynamic model.

In the model, there are two countries with tradable and non-tradable goods with competitive labour markets for each country. The tradable goods sector presents higher relative productivity, and workers’ mobility in between both productive tradable and non-tradable sectors is perfect. PPP is valid only for tradable goods but non-tradable goods prices are different across countries. There is perfect mobility of capital. Tradable and non-tradable production functions YT = ATF (KT,LT) and YNT = ANTF (KNT,LNT), satisfying the following conditions:

A) Constant returns to scale in F(.). Multiplying each input K and L by λ, obtains: AF (λK, λL) → λAF (K, L) for all λ > 0. Where K is capital, L is labour, A is technology and λ is a constant.

B) Positive and diminishing returns to private inputs. Calculating derivatives of F(.) with respect to each input:

ӘF / ӘK = r > 0, Ә2F / ӘK2 < 0

ӘF / ӘL = w > 0, Ә2F / ӘL2 < 0


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Where r is the marginal product of capital and w is the marginal product of labour.

C) Inada condition. In the limit, the first derivatives of F (.) with respect to each input satisfying the following conditions:

LimK→0 (ӘF / ӘK) = limL→0 (ӘF / ӘL) = ∞

LimK→∞ (ӘF / ӘK) = limL→∞ (ӘF / ӘL) = 0

Note that the marginal product of each input depends on the capital-labour ratio k=K/L. Moreover, Y = AF (K,L) → Y = ALF (K/L,L/L) → Y = ALF(K/L,1) → Y = ALF(k,1) → Y = ALf(k) → Y = ALf(K/L).

ӘY / ӘK = ӘALf(K/L) / ӘK = A[Lf’(K/L) * (1/L)] = A(L/L)f’(K/L) = Af’(K/L) = Af’(k). (1)

ӘY / ӘL = ӘALf(K/L) / ӘL = A[(1 * f(K/L)) + Lf’(K/L) * ((0*L-K*1)/L2) = A[f(K/L) + (L/L)f’(K/L) * (-K/L)] = A[f(K/L) - f’(K/L) (K/L)] = A[f(k) - f’(k)k]. (2)

The firm maximization problem is the following:

Maximize profit () = t∞ (1 / (1+z))t [P * AF(K,L) - wL - rK], such that conditions A, B and C are satisfied.

Where z is the discount factor, P is the price of goods and services, w are the wages to workers, r is the capital price and, it is assumed for simplicity, that capital depreciation is equal to zero.

Rewriting equations (1) and (2), first order conditions are the following:

Ә / ӘK = 0 → P * Af’(k) - r = 0 → r = P * Af’(k) (3)

Ә / ӘL = 0 → P * A[f(k) - f’(k)k] - w = 0 → w = P * A[f(k) - f’(k)k] (4)

Tradable goods sector:

r = PT * ATf’(k)

w = PT * AT[f(k) - f’(k)k]

Non-tradable goods sector:

r = PNT * ANTf’(k)

w = PNT * ANT[f(k) - f’(k)k]

Where T is tradable goods and NT are non-tradable goods. It is assumed that the level of prices is defined in geometric averages with weights equal to γ and 1-γ for tradable goods prices and non-tradable goods prices respectively.

PD = PDTγ * PDNT

1-γ (5)

PE = PETγ * PE’NT

1-γ (6)

Where PD is goods and services at domestic prices and PE is goods and services at foreign prices.

Taking into account the perfect mobility of labour in between both tradable and non-tradable productive sectors, the following is obtained for each country:

PDT * ADT[d(k) - d’(k)k] = w = PDNT * ADNT[d(k) - d’(k)k] (7)

PET * AET[g(k) - g’(k)k] = w = PENT * AENT[g(k) - g’(k)k] (8)

Where D is the domestic country and E is the foreign country. Without losing generalization, tradable goods prices can be equal to the numeraire (PT

γ = P’Tγ = 1). Rewriting (7) and (8):


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PDNT = ADT[d(k) - d’(k)k] / ADNT[d(k) - d’(k)k] (7)

PENT = AET[g(k) - g’(k)k] / AENT[g(k) - g’(k)k] (8)

Similarly, rewriting (5) and (6):

PD = (1)γ * PDNT1-γ = PDNT

1-γ (9)

PE = (1)γ * PENT1-γ = PENT

1-γ (10)

Real exchange rate is defined as: RER = c1 * [(NER * PE) / PD]. Using the PPP assumption in the tradable goods market and substituting the numeraire: PT = NER * P’T → 1 = NER * 1 → NER = 1. Finally, RER = c1 * [PE / PD] (11)

Substituting (7) and (8) on (9), (10) and (11) the Balassa – Samuelson effect can be obtained:

RER = c1 * AET[g(k) - g’(k)k] / AENT[g(k) - g’(k)k] 1-γ

------------------------------------------------

ADT[d(k) - d’(k)k] / ADNT[d(k) - d’(k)k]

In short, if an increase in tradable goods productivity, relative to non-tradable goods productivity, is higher in the domestic economy than in the foreign economy, the RER decreases and has an appreciation.

The Balassa - Samuelson theory is usually related to two assumptions: 1) Non-tradable goods prices grow faster than tradable goods prices and, 2) the productivity growth rate of tradable goods relative to the non-tradable goods productivity is higher in countries which are tradable goods are intensive. According to this theory, the prices of non-tradable goods grow higher than tradable goods price growth because the productivity growth rate of tradable goods is higher than the productivity growth rate of tradable goods. If the growth rate of the productivity tradables- non-tradables ratio is higher in domestic economies than in foreign economies, the domestic economy RER decreases or has an appreciation.

The nominal exchange rate offsets or compensates only changes in prices of tradable goods. Considering a non-tradable sector intensive in labour, and a tradable sector intensive in capital, the Balassa - Samuelson theory explains that domestic economic growth increases technological progress and improves tradable goods productivity levels relative to non-tradable goods productivity. This productivity improvement in the domestic economy, relative to the foreign economy, decreases the RER.

The theory maintains that changes in nominal exchange rates do not necessarily oppose international prices ratios to maintain RER constant over time. The core proposes a relationship between RERe and the following real variables:

RERe = c4TT + c5PE + c6NFA + c7BT + c8TAR + c9PR (iv)

Where TT is terms of trade, PE is government expenditure, NFA is net foreign assets, BT is trade balance or openness level, TAR is tariffs and PR is productivity. There is a monetary variable in the lineal model to test the interest rate differential or country risk influence over the RERe.

RERe real determinant signs are the following:

a) Terms of trade (TT): An increase in the international relative price of imports, as a proxy of TT, implies the following effects over RER:


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a.1) Income effect: An increase in export prices (or a decrease in import prices) augments TT and improves wages, encouraging goods and services consumption. This productivity growth in tradable goods or an increase in non-tradable goods prices decreases or appreciates RER (negative relationship).

a.2) Substitutive effect: A decrease in export prices (or an increase in the import prices) decreases TT and increases substitution of foreign goods by domestic goods. This productivity growth in tradable goods or the increase in the price of non-tradable goods decreases or appreciates RER (positive relationship).

The relationship TT and RER depends on the dominant effect.

b) Government expenditure (PE): The PE effect over the RER behaviour depends on the expenditure composition in tradable or non-tradable goods. This effect also depends on how governmental expenditure is financed as levels of investment, consumption and resources can be modified in the private sector. An increase in public expenditure implies the following effects over RER:

b.1) Direct effect: An increase in the government demand of domestic goods and services incentivises domestic production to grow. This productivity growth in tradable goods or an increase in the price of non-tradable goods decreases or appreciates RER (negative relationship).

b.2) Indirect effect: If the increase in the government demand of domestic goods and services overshoots private consumption, the productivity decrease in tradable goods or the decrease in the price of non-tradable goods, will lead to an increase or depreciation of RER (positive relationship).

The relationship PE and RER depends on the difference between both the marginal domestic propensity to consume in both the public and private sectors.(LEO this doesn’t make sense!!)

c) Net foreign assets (NFA): This variable is a measurement of the wealth of national agents in a foreign currency. There are two transmission mechanisms to the RER:

c.1) Income effect: Any increase in net foreign assets increases wealth and domestic consumption levels. This growth in the productivity of tradable goods, or an increase in the price of non-tradable goods, decreases or appreciates RER (negative relationship).

c.2) Substitutive effect: A fall in net foreign assets decreases savings and investment levels and increases domestic consumption. The productivity growth in tradable goods or the increase in non-tradable goods prices decreases or appreciates RER (positive relationship).

The relationship NFA and RER depends on the dominant effect.

d) Trade balance (BT): This variable represents the level of trade openness. It is a measure of wealth of national agents in a foreign currency. There are two transmission mechanisms to the RER:

d.1) Income effect: An increase in the level of trade openness (through diminishing tariffs or improving international trade bureaucratic procedures) decreases international trade discretional distortions and augments wealth, increasing private domestic consumption. This productivity growth in tradable goods or the increase in non-tradable goods prices decreases or appreciates RER (negative relationship).

d.2) Substitutive effect: A decrease in the level of trade openness (through diminishing tariffs or improving international trade bureaucratic procedures) decreases international trade discretional distortions and augments wealth, increasing private foreign consumption. This productivity growth in foreign tradable goods or increase in foreign non-tradable goods prices increases or depreciates RER (positive relationship).

The relationship BT and RER depends on the dominant effect.


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e) Tariffs (TAR): This variable represents the degree of trade openness of an economy. There are two mechanisms of transmission to the RER:

e.1) Income effect: A decrease in tariffs improves the degree of trade openness, reducing discretionary distortions in international trade, increasing wealth and private consumption of domestic goods and services. An increase in the productivity of domestic tradable goods, relative to non-tradable goods (or domestic non-tradables goods’ prices increases) decreases RER (positive relationship).

e.2) Substitution effect: A decrease in tariffs improves the degree of trade openness, reducing discretionary distortions in international trade, increasing wealth and rising private consumption of foreign goods and services. An increase in productivity of foreign tradable goods, relative to non-tradable goods (or foreign non-tradable goods prices increases) increases RER (negative relationship).

The relationship TAR and RER depends on the dominant effect.

f) Productivity (PR): An increase in productivity implies an improvement in the production capacity in the economy and allows for an increase in its level of economic activity. An increase in the productivity of tradable goods or an increase in the price of non-tradable goods diminishes or appreciates the RER (negative relationship).

3. Access to markets and tariffs

The General Agreement on Tariffs and Trade (GATT), established in 1947 and signed in 1948, developed a set of international trade rules to permit and encourage tariff concessions between countries. Since its inception and until the creation of the World Trade Organization (WTO), GATT’s eight rounds have taken into account a schedule of meetings, a set of trade principles and specific methodologies during their multilateral negotiations (see Table 1).

During the first five rounds (1947, 1949, 1951, 1956 and 1960), the regulation plan only included a reduction in tariffs. These rounds were characterized by meetings between a small numbers of countries negotiating each of the trading products. Since there were a large number of items, this level of detail (product by product) was difficult to follow.

From the Kennedy Round to the Uruguay Round, negotiations began to include not only tariff reductions but also other duties and trade barriers such as subsidies, property rights, textiles, agriculture, etc. During the Kennedy Round (from 1964 to 1967) and after a major reform of U.S. domestic laws, it was agreed that tariff reductions were calculated by using the linear formula, thereby protecting sensitive products or exceptions to the general rule. During the Tokyo Round (from 1973 to 1979), it was convenient to begin to apply the "Swiss" methodology, which balanced the effect of the first agreed tariffs reductions in the lowest tariffs. In this round, sensitive products and agricultural products were negotiated at different meetings.


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Year Place Countries Issues

1947 Geneva 23 Tariffs

1949 Annecy 13 Tariffs

1951 Torquay 38 Tariffs

1956 Geneva 26 Tariffs

Dillon Round 1960-1961 Geneva 26 Tariffs

Kennedy Round 1964-1967 Geneva 62 Tariffs and subsidies

Tokyo Round 1973-1979 Geneva 102 Tariffis and other

trade barriers

Uruguay Round 1986-1994 Geneva 123 Tariffis and other trade

barriers, property rights,

agriculture, textiles,

WTO's establishment.

Source: WTO

General Agreement on Tariffs and Trade

Table 1: Rounds of negotiations on trade and tariffs

Finally, the Uruguay Round was the largest international trade negotiation and the most far-reaching reform of the global trading system in terms of scale, not only because it was attended by 123 participating countries from 1986 to 1994, but also because negotiations covered almost all the issues related with commerce and trade. During this round, mainly import duties on tropical products exported by developing countries were reduced, rules for dispute settlement were reviewed and it was established that members should report periodically about their commercial policy’s transparency. Table 2 shows some of the results, sorted by type of country. If we take only industrial products into account, the tariff agreements stipulated varying degrees of reductions depending on the type of country and" five years of tariff reductions at a yearly rate of 20% after WTO’s foundation were agreed” (Millet, 2001). On average, the tariffs of developed countries decreased by 40% those of developing countries by 20% and those of countries in transition by 30%. Property rights increased in percentage terms. Over the total amount of tariff lines, developing countries increased consolidated items from 15% to 58% while those of countries in transition rose from 74% to 96%.

Industrial

Products Before UR After UR Reduc. Before RU After RU

Developed countries 6.8 3.8 40.0 94.0 99.0

Developing Countries 15.3 12.3 20.0 15.0 58.0

Countries in transition 8.6 6.0 30.0 74.0 96.0

Source: Millet 2001 according to World Bank's data

(UR) The Uruguay Round

Table 2: The Uruaguay Round Tariff Agreements

Tariff reduction (%) Consolidated rates (%)

The Uruguay Round ended with the enactment of the WTO’s foundation in 1995 and planned new aims for the future. Some of these goals were consolidated in subsequent years and others were modified. The timetable of the main issues in the negotiations can be observed in Table 3.


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Table 3: Uruguay Ronda's Built-in agenda

1996

- Maritime services: market access negotiations to end (30 June 1996, suspended to 2000,

now part of Doha Development Agenda)

- Services and environment: deadline for working party report (ministerial conference, December 1996)

- Government procurement of services: negotiations start

1997

- Basic telecoms: negotiations end (15 February)

- Financial services: negotiations end (30 December)

- Intellectual property, creating a multilateral system of notification and registration of geographical

indications for wines: negotiations start, now part of Doha Development Agenda

1998

- Textiles and clothing: new phase begins 1 January

- Services (emergency safeguards): results of negotiations on emergency safeguards to take effect

(by 1 January 1998, deadline now March 2004)

- Rules of origin: Work programme on harmonization of rules of origin to be completed (20 July 1998)

- Government procurement: further negotiations start, for improving rules and procedures (by end of 1998)

- Dispute settlement: full review of rules and procedures (to start by end of 1998)

1999

- Intellectual property: certain exceptions to patentability and protection of plant varieties: review starts

2000

- Agriculture: negotiations start, now part of Doha Development Agenda

- Services: new round of negotiations start, now part of Doha Development Agenda

- Tariff bindings: review of definition of “principle supplier” having negotiating rights under

GATT Art 28 on modifying bindings

- Intellectual property: first of two-yearly reviews of the implementation of the agreement

2002

- Textiles and clothing: new phase begins 1 January

2005

- Textiles and clothing: full integration into GATT and agreement expires 1 January

Source: WTO

According to the WTO, the following principles must be taken into account when carrying out tariff reductions:

a) Freer trade: This principle was agreed on the basis of mutual concessions. The reductions were arrived at gradually through negotiations between the parties concerned, thereby lowering trade barriers and encouraging trade. The barriers concerned include customs duties and measures such as import bans or quotas that restrict quantities selectively. Countries with lower tariffs have less bargaining power.

b) Promoting fair competition: This principle is used when a country is a major provider of a product and requests the opening of negotiations on that particular product. The WTO is described as a free trade institution but the system does allow tariffs and other forms of protection. This principle can extend to seconds tariff benefits to countries through the provision of unconditional MFN. That is, the conditions that are applied to commercial nations have fewer restrictions should be applied to all other nations.

c) Encouraging reciprocity and economic reform: This principle established that tariff concessions must be equivalent in terms of bilateral trade growth and mutual benefits. Although, this principle protects countries with lower levels of development, bilateral agreements are influenced by the negotiating capacity of countries. Developing countries need flexibility while the system’s agreements are implemented. This fact allows for trade concessions for developing countries.

d) Predictability and consolidation: This principle was established to monitor the agreement upon completion of the negotiations as arbitrated by the WTO through binding and transparency principles?.


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Moreover, promising not to raise a trade barrier can be as important as lowering one, because the promise gives businesses a clearer view of their future opportunities. After countries agree to open their markets, they are bound to their commitments. For goods, these commitments amount to ceilings on customs tariff rates.

e) Most favored nation (MFN): This principle was introduced in the Kennedy Round. It says that under the WTO agreements, countries cannot discriminate between their trading partners. To grant one country a lower customs duty rate for one of their products means offering the same policy to all other WTO members. Some exceptions are allowed and developing countries need not comply with the principle of reciprocity.

Furthermore, tariff reductions were carried out using the following methodologies:

1) Product by product: This methodology involves the analysis of each of the decreasing tariffs or customs duties. It means that the tariff reductions are discussed item by item.

2) Linear reductions: "The system’s idea is to agree on a certain percentage of tariff reduction to be applied to all products (uniform). To avoid problems with sensitive products, the pure linear system is altered to negotiate reductions by category of products (linear system for each product category) (Milet, 2001).”

3) "Swiss" formula: This methodology consists of a harmonizing formula that is effective at reducing tariff peaks and tariff escalations. This means reductions in tariff ceilings with progressive reductions in applied tariffs, taking into account a final tariff, an initial tariff and a reduction factor (item to be negotiated).

4) Girard or WTO formula: It is a variant of the" Swiss" formula. This methodology consists of a harmonizing formula with a specific coefficient that can be varied to reflect different levels of initial tariffs.

5) "Capping" formula: This is a mechanism which sets groups of countries according to their ability to adjust to the new tariff reductions. There are three categories of countries: leading countries (lowest coefficient "1-a"), followers (moderate coefficient "1-a") and incoming countries (highest coefficient "1-a").

After the WTO’s foundation, the system to reduce tariffs has changed significantly. The new methodology is based on rounds of negotiations carried out in ministerial conferences. These negotiations are characterized by a single contractual agreement, a unique dispute settlement body, a new leadership, greater transparency and better performance.

Furthermore, the WTO specified a new regulation. First, there will be a unique new contract that sets all negotiations, which will apply to every country member (except for multilateral agreements). Therefore, developing countries cannot decide whether or not to sign an agreement. Second, a single arbitrage body which blocks functional group of panels to solve disputes was established. Third, WTO will achieve greater transparency and surveillance through the new Trade Policy Review Mechanism. Fourth, WTO’s General Directors will be political figures instead officials appointed by the country members. Finally, it would be different from the former regulation because it will be compulsory for signatory countries to meet at least once every two years. Since the creation of the WTO, there have been the following ministerial conferences:

Singapore (1996): A ministerial declaration was announced and 18 countries signed agreements on trade of information technology products.


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Geneva (1998): A ministerial declaration and a general statement on electronic commerce were re-announced.

Seattle and the Millennium Round (1999): There were no statements issued at the end of this round.

Doha Round (2001): Several declarations and decisions known as the "Agenda for the Doha Round" were announced.

Cancun (2003): No declaration was issued due to disagreements amongst developed countries about the G-21’s position calling for the elimination of agricultural subsidies in developed countries.

Hong Kong (2005): A ministerial declaration and a list of questions for ministers which included 5 points directly related to protectionism in agriculture and other 3 on non-agricultural products, were announced.

Of the last ministerial conferences, the most relevant one proved to be the so-called Doha Round in 2001. This round aimed to continue and complete the goals set at the Uruguay Round, through the implementation of agreements still current in 2001 and the improvement of agricultural trade negotiations transparency.

Two different aspects characterized the Doha Round On the one hand; representatives of developing economies (with large and growing markets) imposed high ceilings and tariffs, thus requiring greater transparency in agricultural and commodities markets. On the other hand, representatives of developed economies with low ceilings and tariff rates levels had only a narrow margin for negotiation.

In the Doha Declaration, there are 21 topics related to actual negotiations, implementation of agreements, economic and political analysis and agreements’ surveillance. Major topics include issues about agriculture, services, sanitary and phytosanitary measures, textiles and clothing sectors, access to markets for non-agricultural products, measures on investment related to trade, antidumping and subsidies, customs valuation, intellectual property rights, trade and competition policy, transparency in government procurement, regional trade, dispute settlement, trade and environment, electronic commerce, small economies and public finances.

The Doha Round did not have the expected output and country members decided to encourage tariff reductions through bilateral negotiations. In this general scenario, negotiations may take place in the Trade Negotiations Committee, in other WTO’s boards and committees or in external meetings. Some of the G7 countries have negotiated tariff reductions directly with other markets, thus establishing bilateral agreements with emerging economies such as those belonging to the BRIC countries.

In this context, Table 4 presents statistical data ad valorem under the harmonized system for the simple average tariff rates, according to the principle of Most Favoured Nation (MFN) in the following countries: Brazil, Russia, India and China. (Note that the source includes the harmonized system of Trade Analysis and Information (TRAINS) through the WITS program of the United Nations Conference on Trade and Development (UNCTAD), the Integrated Data Base (IDB) through the WITS program of the World Trade Organization (WTO) and the World Bank.) Specifically, the statistics were obtained using a simple average of applied import tariffs charged ad valorem at the national level line under the most favoured nation principle. The statistics of some ome years have been completed using applied tariff under 6 digits. Annual data have been quarterized with the time series of imports unit value.


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Year Brazil Russia India China

1996 19.99 10.89 37.00 23.65

1997 11.95 12.63 30.09 17.61

1998 14.56 13.90 n.d. 17.48

1999 14.37 12.60 32.95 17.11

2000 14.13 11.10 33.65 16.95

2001 14.80 10.26 32.32 15.89

2002 12.28 9.81 29.00 12.38

2003 12.03 n.d. n.d. 11.25

2004 11.89 n.d. 29.94 10.42

2005 10.75 9.65 18.86 10.67

2006 12.33 n.d. 15.05 9.68

2007 10.68 9.01 17.08 9.78

2008 11.26 8.78 12.43 9.43

Variation 96-08 (%) -43.68 -19.44 -66.39 -60.15

Source: UNCTAD (TRAINS - WITS) / WTO (IDB - WITS) / World Bankl (siteresources.worldbank.org)

(*) Some years have been completed using applied tariffs under 6 digits. (n.d.) = no available

MFN = Most Favoured Nation

Table 4: Tariffs. Simple average ad valorem data from the harmonized system

Applied tariffs at national levels under the MFN principle*

4. Methodology1

Basically, there are three econometric procedures. The first procedure performs the augmented Dickey - Fuller test to find unit roots. The optimum lags order is calculated running the Schwarz information criterion, and the critical values are based in MacKinnon to 1%, 5% and 10%. The presence of structural breaks is tested through the Chow test. The second procedure is the Johansen method to test the number of co-integration vectors under unrestricted intercepts and restricted tends assumptions. Finally, the third procedure is an algorithm to minimize the sum of squared residuals of a lineal regression under estimator efficient properties. The ordinary least squares (OLS) model includes the stationary vectors found through using the cointegration procedure. The adjustment of the model to the data taking into account the R2 is considered, as is the individual significance of the estimators, the autocorrelation, the heteroskedasticity and the normality of the errors.

The econometric methodology is justified for two main reasons:

1) In order to test if nominal exchange rates are compensated by changes in domestic and foreign prices, it is necessary to exam the RER behavior over time and to assess the validity of equation (iii) presented in the theoretical framework: RERe = c1 * c2 = c3. An augmented Dickey - Fuller (1979) unit roots test as a null hypothesis and MacKinnon (1991) critical values to 1, 5 and 10% to evaluate stationarity in the RER (where parameters of the lagged variables are not statistically significant and

where the errors satisfy condition iid (0,2)) validates the equation (iii). Otherwise, no rejection of the

parameters statistical significance in the RER lagged would infer that there are determinants which could explain the behavior of RER in levels.

2) In order to find if those determinants are linked to the RER through structural mechanisms, it is convenient to introduce the Balassa - Samuelson effect in this theoretical framework, thereby showing that RER changes are explained by real economy variables.

1 See Tariffi (2010) for further discussions.


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As described in the theoretical framework, an increase in A[i(k) - i’(k)k] (where i=g,d) in tradables (relative to non-tradables) is higher in the domestic economy than in the foreign economy, the ERE decreases. The transmission mechanism is as follows:

(AET / AENT) = (PDNT1-γ) TCR (domestic currency increases)

(ADT / ADNT) = (PDNT1-γ) TCR (domestic currency increases)

Those variables, which affect relative productivity between countries and between tradable and nontradable sectors, will also affect relative prices between countries and between tradable and non tradable sectors and will ultimately affect the RER. These determinants and their parameters signs are presented in equation (iv) of the theoretical framework: RERe = c4TT + c5PE + c6NFA + c7BT + c8TAR + c9PR. This relationship could exist in both the short or long term

An autorregresive vector (VAR) defined by Engle and Granger (1987) with the Johansen (1988, 1991) methodology under unrestricted intercepts and restricted trends assumptions by Pesaran, Shin and Smith (2000), evaluates the long run relationship of equation (iv) when a algorithm minimizing the sum of squared residuals of a lineal regression under estimator efficient properties (OLS method with ECM) determines the short-term relationship.

5. Empirical evidence

The applicability of the theoretical model is tied to the relationship between the U.S. dollar and currencies belonging to BRIC countries: the real, ruble, rupee and yuan. It is followed by 4 steps to develop the empirical work. First at all, the data is defined and calculated; second, the unit root time series and the PPP theory are tested; third, the long- term run relationship between the cointegrated variables to build the short term model with MCE is shown and; finally, the relationship between the RER, tariffs and the other real determinants is estimated.

Data

The quarterly data starts in 1993:1 until 2008:4 for Brazil, India and China, and from 1995:1 to 2008:4 for Russia (see tables 5, 6, 7 and 8 in annexes). This frequency of the data is due to the fact that most of the "proxies" variables are published quarterly. For instance, one of the key "proxies" to explain RER through real determinants and the Balassa - Samuelson effect is the Gross Domestic Product (GDP). The GDP is published mostly on a quarterly basis.

The period of analysis is mainly limited by two facts:

a) There is no data available belonging to tariffs before the first year.

b) After the global financial crisis, the data showed a highly fluctuating pattern of behavior.

The base year data is 2005 and primary sources are the following:

1) The international finances statistics from the International Monetary Fund (IFS/IMF).

2) The macroeconomics data from the Statistical Office of the European Communities (Eurostat - European Commission) and from the European Central Bank (ECB).

3) The inflation data from the Bureau of Labor Statistics of the United States (BLS).


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4) The Integrated Data Base (IDB) through the software World Integrated Trade Solution (WITS) from the World Trade Organization (WTO).

5) The Trade Analysis Information System (TRAINS) through the software World Integrated Trade Solution (WITS) from the United Nations Conference on Trade and Development (UNCTAD).

6) The Management System for Time Series of the Central Bank of Brazil (BDB).

7) The Federal State Statistics Service of Russia (Rosstat).

8) Reuters.

The proxy of the variables is the following:

Variable RER: The real exchange rate is calculated by multiplying the nominal exchange rate of each BRIC country by its consumer price index respectively over the consumer price index of United States. RER = NER*CPI/CPIUSA.

Variable TT: The terms of trade are obtained from the ratio unit value exports over unit value imports divided by the gross domestic product for each country belongs to BRIC. TT = (UVX/UVI)/GDP.

Variable PE: The proxy of public expenditure is the current government expenditure for each BRIC’s country dived by its consumer price index respectively. PE = PE/CPI.

Variable NFA: The net foreign assets are calculated dividing the international reserves plus gold for each BRIC’s country, divided by the consumer price index of the United States. In the China case, foreign assets replace the international reserve plus gold. NFA = IR+Gold/CPIUSA.

Variable BT: The balance of trade is the sum of the volume of exports plus the volume of imports divided by the gross domestic product for each country belonging to BRIC. BT = (M+X)/GDP.

Variable TAR: Tariffs are obtained from the harmonized system, by calculating the simple average of the applied custom rate at a national level of the ad valorem import tariff according to the principle of Most Favoured Nation (MFN). Some years have been completed using applied tariff under 6 digits. Annual data have been quarterized with the time series of imports unit value.

Variable PR: Productivity is obtained by dividing the gross domestic product for each BRIC country by its number of full time employees respectively. PR = (GDP/Nº of Em.).

Rewriting equation (18) as the model (iv) in the theoretical framework:

RERe = f(t) + c4TT + c5PE + c6NFA + c7BT + c8TAR + c9PR + MCE + ut (19)

Unit roots test

The unit roots tests contrast the purchasing parity power, observing if the real exchange rate deviations, with respect to its equilibrium, are transitory or permanent.


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Figure 2: Real exchange rate - Brazil

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Figure 5: Real exchange rate - China

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Through the unit root Dickey - Fuller augmented test at 1, 5 and 10%, results can be obtained taking into account data in levels, , in first difference and in second differences for the following cases: a) including intercept, b) including intercept and trend and, c) not including intercept neither trend (see tables 9, 10, 11 and 12 in annexes). The optimal lag is calculated through the Schwartz information criterion. As the data is quarterly, the maximum number of lags included in the tests is 9. Evaluating RER in levels for each of the BRIC countries, a stationarity test shows that the null hypothesis of the unit root cannot be rejected. Stationarity tests also show that all RER time series are integrated with order 1 or they are I(1), with the only exception of Russia whose time series is integrated with order 2 or I(2). From figure 1 to 5, it is evident that the behavior of the RER is not stationary in variance.

In order to evaluate stability in stationary models, a Chow test was applied. Breaking points correspond to the dates where BRIC countries became members of the World Trade Organization (WTO). Brazil and India signed the agreement in 1995 and China in 2001. In the case of Russia, the break point was set in 2005. Even though Russia only began negotiations to join as a member of the WTO in 1995, it signed significant international agreements were during in the 27th Session of the Contracting Parties on April 15, 2005. During this year, the Russian Federation established negotiations on trade with 28 members of the WTO and the European Community, thereby signing agreements about 87% of Russian imports.

Considering that there is no evidence of structural breaks in RER stationary models, the fact that the unit root null hypothesis cannot be rejected justifies the next step in the methodological procedure: to find empirical determinants along the lines of the model (19).

Table 9, 10, 11 and 12 also show optimal lag levels to assess whether determinats follow a path of stationarity. In the Brasil model, TT, NFA, BT, TAR and PR are I(1) although variable PE is I(0). An augmented Dickey Fuller test does not clearly evaluate stationarity without trends intercepting in the case of TT and NFA. The Russia model presents TT, PE, BT and TAR integrated with order 1 although AEN is I(0) and PR is I(2). Since results are not conclusive when stationarity of PR in first differences is evaluated, stationarity of the time series PR in second differences has also been tested. In the case of India, all variables are I(1) on the right hand of the equation. In first differences, the TT data is stationary only when the intercept and the trend in the model is included, the NFA is weakly stationary, at 10%, with intercept or intercept and trend in the regression and, the time series BT is not stationary when there is no trend nor intercept. Finally in China, all the real determinants are I(1) despite NFA which is I(0). The variable TT is stationary in levels when unit roots are evaluated without intercept or trend and BT, TAR and PR have the same property with different levels of significance.


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Cointegration test

In order to find the long run relationship between variables RER, TT, PE, NFA, BT, TAR and PR for each BRIC country, a VAR model of order 1 with unrestricted intercept and restricted trend was run. Tables 13, 14, 15 and 16 show the ratio of maximum likelihood with significance levels at 5% and 10% for each country. In the case of Brazil, the variable PE is not included and there are two cointegrating vectors. The trace test also allows three cointegrating vectors in Russia when NFA is not included in the model. The inclusion of all real determinants resulted in four cointegrating vectors for India. And finally in China, there are two cointegrating vectors excluding the NFA time series.

Moreover, table 17, 18, 19 and 20 (see annexes) present values of VAR estimators, respectively for each country cointegrating vectors. Normalized values are in parentheses. All vectors found are used to calculate error correction mechanisms.

Table 13: Brazil. Cointegration with unrestricted intercepts and restricted trends in the VAR

Cointegration LR Test Based on Trace of the Stochastic Matrix

*******************************************************************************

63 observations from 1993Q2 to 2008Q4. Order of VAR = 1.

List of variables included in the cointegrating vector:

RER TT NFA BT TAR PR Trend

List of eigenvalues in descending order:

.65756 .51727 .35311 .23554 .087576 .047107 .0000

*******************************************************************************

Null Alternative Statistic 95% Critical Value 90% Critical Value

r = 0 r>= 1 166.5728 115.8500 110.6000

r<= 1 r>= 2 99.0588 87.1700 82.8800

r<= 2 r>= 3 53.1766 63.0000 59.1600

r<= 3 r>= 4 25.7351 42.3400 39.3400

r<= 4 r>= 5 8.8139 25.7700 23.0800

r<= 5 r = 6 3.0399 12.3900 10.5500

*******************************************************************************

Use the above table to determine r (the number of cointegrating vectors).

Table 14: Russia. Cointegration with unrestricted intercepts and restricted trends in the VAR


*******************************************************************************



RER TT PE BT TAR PR Trend


.75049 .56635 .40603 .35360 .19516 .087990 .0000

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r = 0 r>= 1 188.4741 115.8500 110.6000

r<= 1 r>= 2 113.5080 87.1700 82.8800

r<= 2 r>= 3 68.3901 63.0000 59.1600

r<= 3 r>= 4 40.2599 42.3400 39.3400

r<= 4 r>= 5 16.6975 25.7700 23.0800

r<= 5 r = 6 4.9736 12.3900 10.5500

*******************************************************************************



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Table 15: India. Cointegration with unrestricted intercepts and restricted trends in the VAR


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RER TT PE NFA BT TAR PR Trend


.75591 .64087 .52477 .46909 .21313 .14203 .10191 .0000

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r = 0 r>= 1 271.6413 147.2700 141.8200

r<= 1 r>= 2 182.7979 115.8500 110.6000

r<= 2 r>= 3 118.2819 87.1700 82.8800

r<= 3 r>= 4 71.4126 63.0000 59.1600

r<= 4 r>= 5 31.5233 42.3400 39.3400

r<= 5 r>= 6 16.4224 25.7700 23.0800

r<= 6 r = 7 6.7713 12.3900 10.5500

*******************************************************************************


Table 16: China. Cointegration with unrestricted intercepts and restricted trends in the VAR


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.58274 .49416 .34506 .24274 .12370 .060307 0.000

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r = 0 r>= 1 154.4179 115.8500 110.6000

r<= 1 r>= 2 99.3531 87.1700 82.8800

r<= 2 r>= 3 56.4168 63.0000 59.1600

r<= 3 r>= 4 29.7546 42.3400 39.3400

r<= 4 r>= 5 12.2375 25.7700 23.0800

r<= 5 r = 6 3.9187 12.3900 10.5500

*******************************************************************************


Ordinary least squares (OLS) model with the error correction mechanism (ECM)

An OLS model with ECM determines the short-term relationship between the real exchange rate, tariffs and the other key determinants. For each BRIC country, an interactive process with four distributed lags finds the empirical model. Error correction mechanisms have been calculated from the cointegrated vectors, lagged one period and included in the OLS models with a negative sign. Tables 21, 22, 23 and 24 show the results. Where, D stands for first difference and the negative number in parentheses is the lag of the variable.

Table 21 shows the Brazil model. All variables are transformed into first differences except for public expenditure, which is in levels. One of the two correction mechanisms is not found to be significant. R2 is greater than 50%. Variables lagged DBRATCR and DBRAAEN have a highly significant p-value associated to its t-statistics. There is a significant seasonal variable (S2).


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Table 21: Dependent Variable: DBRARER Method: Least Squares Sample(adjusted): 1994:1 2008:4 Included observations: 60 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob.

C 2.930435 1.154277 2.538763 0.0144 DBRARER(-1) 0.599910 0.131236 4.571225 0.0000 DBRARER(-2) -0.374113 0.138092 -2.709167 0.0093 DLBRATT(-1) -0.269030 0.150172 -1.791477 0.0795 DLBRATT(-3) 0.245992 0.147025 1.673130 0.1008 DBRANFA(-2) -0.000651 0.000208 -3.126947 0.0030 DBRABT(-3) -0.259660 0.104511 -2.484533 0.0165

DBRATAR(-1) -0.019976 0.010599 -1.884709 0.0655 DBRAPR -483.9492 175.5078 -2.757422 0.0082

DBRAPR(-1) 451.3190 180.6713 2.498012 0.0160 ECM2 -0.068363 0.027222 -2.511296 0.0154

S2 0.148690 0.086565 1.717669 0.0923

R-squared 0.519028 Mean dependent var 0.041004

Adjusted R-squared 0.408806 S.D. dependent var 0.179163 S.E. of regression 0.137757 Akaike info criterion -0.949792 Sum squared resid 0.910898 Schwarz criterion -0.530923 Log likelihood 40.49376 F-statistic 4.708907 Durbin-Watson stat 1.434014 Prob(F-statistic) 0.000074


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Table 22: Dependent Variable: D2RUSRER Method: Least Squares Sample(adjusted): 1996:3 2008:4 Included observations: 50 after adjusting endpoints


C 6.715707 0.669795 10.02651 0.0000 D2RUSRER(-1) 0.240102 0.101935 2.355447 0.0255 DLRUSTT(-1) -1.647217 0.556769 -2.958529 0.0061 DLRUSTT(-2) -0.926427 0.422403 -2.193231 0.0365 DRUSPE(-2) -0.651467 0.127679 -5.102361 0.0000 DRUSPE(-3) -0.596795 0.138187 -4.318763 0.0002

RUSNFA -0.003603 0.000445 -8.093434 0.0000 RUSNFA(-1) 0.004033 0.000469 8.590763 0.0000

DRUSBT -0.002096 0.001132 -1.852465 0.0742 DRUSBT(-2) -0.002406 0.001306 -1.842045 0.0757 DRUSBT(-3) -0.006931 0.001352 -5.128323 0.0000 DRUSBT(-4) -0.003717 0.001273 -2.920600 0.0067 DRUSTAR 0.184318 0.037890 4.864523 0.0000

DRUSTAR(-2) -0.151613 0.033857 -4.478111 0.0001 DRUSTAR(-4) 0.109493 0.035432 3.090215 0.0044

D2RUSPR -5785.601 1582.146 -3.656805 0.0010 D2RUSPR(-2) -3858.774 760.7631 -5.072241 0.0000 D2RUSPR(-3) -11293.94 1188.658 -9.501424 0.0000 D2RUSPR(-4) -10822.24 1310.620 -8.257344 0.0000

ECM1 -0.707913 0.083832 -8.444465 0.0000 ECM3 -0.336739 0.119663 -2.814070 0.0087

R-squared 0.948630 Mean dependent var 0.116497 Adjusted R-squared 0.913202 S.D. dependent var 1.017755 S.E. of regression 0.299845 Akaike info criterion 0.724172 Sum squared resid 2.607306 Schwarz criterion 1.527221 Log likelihood 2.895708 F-statistic 26.77661 Durbin-Watson stat 1.539097 Prob(F-statistic) 0.000000

Table 23: Dependent Variable: DINDRER Method: Least Squares Sample(adjusted): 1994:2 2008:4 Included observations: 59 after adjusting endpoints


C 40.22194 6.331071 6.353102 0.0000 DLINDTT 3.329282 1.278776 2.603492 0.0121

DLINDTT(-1) -4.326488 1.140941 -3.792034 0.0004 DINDPE(-4) 0.705335 0.159916 4.410646 0.0001 DINDNFA -0.009386 0.001606 -5.842807 0.0000

DINDBT(-2) 0.250251 0.088760 2.819430 0.0068 DINDTAR(-2) 0.114337 0.056290 2.031223 0.0475

ECM3 -0.161809 0.026023 -6.217929 0.0000

R-squared 0.762306 Mean dependent var 0.627441 Adjusted R-squared 0.729681 S.D. dependent var 1.772654 S.E. of regression 0.921641 Akaike info criterion 2.800154 Sum squared resid 43.32057 Schwarz criterion 3.081854 Log likelihood -74.60453 F-statistic 23.36595 Durbin-Watson stat 2.315283 Prob(F-statistic) 0.000000


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Table 24: Dependent Variable: DCHIRER Method: Least Squares Sample(adjusted): 1994:2 2008:4 Included observations: 59 after adjusting endpoints


C 0.561278 0.317317 1.768823 0.0839 DCHIRER(-1) 0.051051 0.019650 2.597990 0.0127 DCHIRER(-2) 0.097075 0.018343 5.292188 0.0000 DLCHITT(-2) 0.547153 0.265196 2.063206 0.0450 DCHIPE(-2) 0.002608 0.000915 2.849295 0.0066 CHINFA(-1) -0.000230 7.53E-05 -3.051715 0.0038 CHINFA(-2) 0.000434 0.000144 3.016183 0.0042 CHINFA(-3) -0.000706 0.000174 -4.058740 0.0002 CHINFA(-4) 0.000510 0.000117 4.374258 0.0001 DCHITAR 0.034223 0.006264 5.463712 0.0000

DCHITAR(-1) 0.015010 0.006259 2.398118 0.0208 DCHITAR(-2) 0.020108 0.006263 3.210698 0.0025 DCHIPR(-4) 1.456317 0.732205 1.988948 0.0529

ECM2 -0.012343 0.004833 -2.554091 0.0142 T 0.007296 0.001584 4.605611 0.0000

R-squared 0.811775 Mean dependent var -0.128502 Adjusted R-squared 0.751885 S.D. dependent var 0.182543 S.E. of regression 0.090927 Akaike info criterion -1.742399 Sum squared resid 0.363777 Schwarz criterion -1.214212 Log likelihood 66.40077 F-statistic 13.55449 Durbin-Watson stat 2.081774 Prob(F-statistic) 0.000000

The short-term model of Russia is shown in Table 22. All variables are placed in first differences except for RER and PR (in second differences) and net foreign assets (in levels). Only one error correction mechanism is not significant. The R2 is relatively high (about 95%).

In the case of India, the model with all variables in first differences and four error correction mechanisms was used.. Since three mechanisms are found to be insignificant, table 23 only presents one. The measurement of R2 is about 76%. Finally, China’s model is presented in table 24. Net foreign asset is the only time series introduced at levels. In between two error correction mechanisms, it is found that ECM2 is significant. R2 is relatively high (about 81%). There is also a statistically significant trend. Apart from the China model, all BRIC regressions include RER lags as a endogenous variable (right hand of the equation), to capture the adjustments of present RER from speculative effects of past values of RER. Error correction mechanisms coefficients are all negative and located between 0 and 1 as predicted by the econometric theory. Note that variables TAR are statistically significant in all BRIC models.

The Breusch - Godfrey serial correlation LM test is presented in table 25. Taking into consideration 2, 3 and 4 lags, the probability associated to the chi-square likelihood statistic test for each lag, the null hypothesis of no autocorrelation for all autoregressive coefficients in the auxiliary regression models of Brazil , India and China cannot be rejected.


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Lag 2 3 4

Brazil

Obs*R-squared 4.80 4.84 6.21

Prob. Chi-Square 0.09 0.18 0.18

Russia

Obs*R-squared 10.66 12.01 13.22


India

Obs*R-squared 2.01 2.02 2.92


China

Obs*R-squared 0.18 5.79 7.43


Source: Author's calculations

Table 25: Breusch-Godfrey Serial Correlation LM Test

The White heteroskedasticity test without cross terms shows a statistic value equal to 24.20514, 40.63355, 23.769 and 29.01221; and a p-value associated with the statistic equivalent to 0.283, 0.487, 0.049 and 0.412 for the model of Brazil, Russia, India and China, respectively. Taking into account that the associated p-values are larger than 1% (significance level), the null hypothesis of no heteroskedasticity cannot be rejected. By using a Jarque - Bera test, normality in the residuals of the models is assessed. There are the following associated p-values: Brazil (0,000), Russia (0,934), India (0,180) and China (0,642). The null hypothesis of normal distribution in the residuals with a significance level of α = 0.01, 0.05 and 0.10 cannot be rejected.

6. Results and conclusions

The unit root test infers non-stationarity on time series RER for each BRIC country and evaluates empirically the PPP theory. This test examines stationary characteristics in real determinants presented in tables 9, 10, 11 and 12. The cointegration test plotted 2 cointegrating vectors in Brazil model, 3 cointegrating vectors in Russia, 4 cointegrating vectors in India and 2 cointegrating vectors in China. After finding long-term relations between the RER and its real determinants, short-term econometric models with error correction mechanisms are estimated. The OLS with ECM methodology estimated coefficients that are statistically significant.

Including 64 observations in the regressions of Brazil, India and China and 56 observations in Russia, the models fit acceptably with the statistical data with R2 values corresponding to 0.52, 0.95, 0.76 and 0.81 for each BRIC countries, respectively. In figures 5, 6, 7 and 8, the solid line (RER variable in levels) and the dashed line (regressed RER) fluctuate in unison.

In Brazil, variable tariff rates (TAR) are significant at 10%, and show a negative sign. With 1 lag, a decrease in TAR increases the real exchange rate, depreciating the real-dollar relationship. With the exception of productivity, all real determinants affect RER with 1, 2 and 3 lags. The joint effect from TT to the RER is explained by the fact that domestic goods are replaced with imported goods after quarters 1 and 3. An increase in net foreign assets or the level of trade openness has a negative effect on RER after 2 and 3 lags, respectively. These facts suggest a causality link. In the case of Russia, the variable RER is affected by TAR in levels and when it is delayed 2 and 4 periods. The combined effect of the


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variable TAR on the RER is positive and highly significant, thereby indicating an appreciation of the ruble-dollar ratio when tariffs decreases. Note that, in opposition to the Brazil model, public expenditure is a statistically significant variable in the Russia regression. After 2 and 3 periods, government demand increases for goods and services in the domestic market stimulates the production level of tradable goods at the domestic economy without displacing private consumption, thereby increasing the price of non- tradable and decreasing the RER.

Figure 5: Short term model adjustment in Brazil

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

94 96 98 00 02 04 06 08

BRATCR BRATCRE

Figure 6: Short term model adjustment in Russia


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0

10

20

30

40

1996 1998 2000 2002 2004 2006 2008

RUSTCR RUSTCRE

Figure 7: Short term model adjustment in India

10

20

30

40

50

60

94 96 98 00 02 04 06 08

INDTCR INDTCRE


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Figure 8: Short term model adjustment in China

6

7

8

9

10

11

12

13

14

15

94 96 98 00 02 04 06 08

CHITCR CHITCRE

The short term regression in the Indian economy shows a positive relationship between TAR and RER. A significant coefficient at 5 and 10% indicates that tariffs reductions are followed by an appreciation of the rupee. The public expenditure effect over RER is positive and takes a year to become effective. After 4 quarters, the indirect effect dominates the direct effect and an expansionary fiscal policy depreciates the RER. In China, the coefficients of the variable TAR both in levels and in lags are positive, not only when they are evaluated individually but also when they are jointly tested. The NFA presents the highest overall joint effect on the endogenous variable with 2 positive lags and 2 negative lags which offset each other. Taking into account that the combined effect of variable TAR on the RER is positive, an increase in foreign assets increases the level of foreign investment and decreases the RER.

A reduction in tariffs on imports adversely affects the RER in Brazil. In order to reduce the effect of the currency depreciation on trade, it is necessary to compensate the rest of the real determinants through discretionary changes. Apart from Brazil, the relationship between TAR and RER variable is positive in the other BRIC countries. Reductions in tariffs in Russia, India and China, in international environments, directly or indirectly modify economic international relations and such changes in reciprocal agreements link rates and the real economy to fluctuations in the RER.

It is clear that BRIC international negotiations at the WTO have helped to reduce their tariffs, and apart from Brazil, they have all positively affected the value of their currencies. Reductions in import tariffs in Russia, India and China have decreased the RER. Taking into account the rigidity of the productivity growth and changes in the underlying real exchange rate, it can be affirmed that only discretionary changes in variables such as public expenditure, balance of trade and terms of trade, are able to balance the equilibrium of the national currencies caused by decreases in tariffs.


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7. Bibliografía:

Balassa, Bela. (1964) The Purchasing-Power Parity Doctrine: A Reappraisal, The Journal of Political Economy, 72(6), Diciembre, pp. 584-596.

Carrion-i-Silvestre, J. Ll., del Barrio, T. y López-Bazo, E. (2004). Evidence on the Purchasing Power Parity in a Panel of Cities, Applied Economics, 36(9), pp. 961-966.

Carrion-i-Silvestre, J. Ll. y Basher, Syed; Price level convergence, purchasing power parity and multiple structural breaks in panel data analysis: An application to U.S. cities, Journal of Time Series Econometrics, manuscript submitted 1000.

Cassel, Gustav. (1918). Abnormal deviation in international exchanges, Economic Journal, 28(112), Diciembre, pp. 413-415.

Clarida, Richard y Galí, Jordi. (1994). Sources of real exchange rate fluctuations: How important are nominal shocks?, National Bureau of Economic Research, Working Paper No. 4658, Febrero.

Conferencia de las Naciones Unidas para el Comercio y el Desarrollo; UNCTAD handbook of statistics, United Nations Publications, 2008.

Dickey, David y Fuller, Wayne. (1979). Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74(366), Junio, pp. 427-431.

Dickey, David y Fuller, Wayne. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root, Econometrica, 49(4), Julio, pp. 1057-1072.

Dornbusch, Rudiger. (1976). La Teoría de los Regímenes de Tipos de Cambio Flexibles y la Política Macroeconómica, Cuadernos de Economía (Latin American Journal of Economics), 13(39), pp. 27-50.

Edwards, Sebastian; Real and Monetary Determinants of Real Exchange Rate Behavior: Theory and Evidence from Developing Countries, UCLA Working Paper No. 506, Septiembre a1988a.

Edwards, Sebastian; The determination of equilibrium real exchange rate, UCLA Working Paper No. 508, Septiembre b1988.

Engle, Robert F. y Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing, Econometrica, 55(2), Marzo, pp. 251-276.

Fleming, J. M. (1962). Domestic financial policies under fixed and floating exchange rates, International Monetary Fund, Staff Papers 9, pp. 369-379.

Fondo Monetario Internacional; International finances statistics, Country notes 2007, IMF Statistics Department, Vol. LX, 2007, pp. 73-75.

Frenkel, Jacob A. (1981). Flexible exchange rate, prices, and the role of “news”: Lessons from the 1970s, The Journal of Political Economy, 89(4), pp. 665-705.

Froot, Kenneth y Rogoff, Kenneth. (1994). Perspectives on ppp and long-run real exchange rates, National Bureau of Economic Research, Working Paper No. 4952.

Johansen, Soren. (1988). Statistical analysis of cointegration vectors, Journal of Economic Dynamics and Control, 12(2-3), Junio-Septiembre, pp. 199-607.

Johansen, Soren. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models, Econometrica, 59(6), Noviembre, pp. 1551-1580.


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MacKinnon, James. (1990). Critical Values for Cointegration Tests, University of California at San Diego, Economics Working Paper Series 90-4.

Messerlin, Patrick. (2006). The Doha negotiations on trade in goods: An European perspective, Mimeo.

Meese, Richard y Rogoff, Kenneth. (1988). Was it Real? The Exchange Rate-Interest Differential Relation Over the Modern Floating-Rate Period, The Journal of Finance, 43(4), Septiembre, pp. 933-948.

Millet, Montserrat. (2001). La regulación del comercio internacional: del GATT a la OMC, Colección Estudios Económicos, vol. 24.

Mundell, R. A. (1964). Exchange Rate Margins and Economic Policy, Money in the International Order, ed. C. Murphy, Southern Methodist University Press.

Obstfeld, Maurice y Rogoff, Kenneth. (1996). Foundations of international macroeconomics, MIT Press (Cambridge, Mass).

Organización Mundial del Comercio; Perfiles arancelarios en el mundo, Secretaría de la OMC, 2007.

Pesaran, M. Hashem; Shin, Yongcheol y Smith, Richard J. (2000). Structural analysis of vector error correction models with exogenous I(1) variables, Journal of Econometrics, 97(2), pp. 293-343, Agosto.

Samuelson, Paul A. (1964). Theoretical Notes on Trade Problems, The Review of Economics and Statistics, 46(2), Mayo, pp. 145-154.

Tariffi, Leonardo (2010). Euro-dollar real exchange rate misalignments: Is the euro overvalued?, China-USA Business Review, 9(7), pp. 1-21.


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8. Annexes. Table 5:

Table 5: Data Brazil (Levels)

Index 2005=100

Year RER TT PE NFA BT TAR PR

NER*CPI/CPIUSA (UVX/UVI)/GDP PE/CPI IR+Gold/CPIUSA (M+X)/GDP Applied MFN (GDP/Nº of Em.).

Q1 1993 0.00002 0.02633 281.3439 296.54263 1.6428 13.94452 0.004523

Q2 1993 0.00010 0.02283 145.2940 323.98511 1.6805 14.45101 0.004659

Q3 1993 0.00052 0.02372 68.5662 355.88925 2.0728 12.31070 0.004922

Q4 1993 0.00316 0.02378 29.9935 424.98160 1.8537 14.40891 0.004898

Q1 1994 0.02304 0.02454 8.5231 503.93421 1.6572 11.95406 0.004461

Q2 1994 0.19802 0.02186 3.0048 562.07206 1.8972 12.55900 0.004596

Q3 1994 0.44236 0.01807 2.2537 564.52813 1.7584 13.41097 0.004778

Q4 1994 0.44394 0.01602 2.4816 502.36465 1.9463 13.42457 0.004737

Q1 1995 0.46816 0.01396 1.8566 431.47908 1.8961 17.26741 0.004305

Q2 1995 0.52218 0.01363 1.7451 425.24422 1.8864 19.38358 0.004786

Q3 1995 0.57136 0.01243 1.7514 616.33777 1.7643 17.80852 0.005048

Q4 1995 0.60707 0.01557 1.9134 655.25831 1.8898 14.58318 0.004972

Q1 1996 0.63145 0.01357 1.4599 695.18225 1.6191 19.98736 0.004688

Q2 1996 0.65657 0.01332 1.5070 743.74203 1.8241 19.69844 0.004834

Q3 1996 0.68123 0.01322 1.6565 722.71749 1.8771 19.52187 0.004952

Q4 1996 0.69598 0.01329 1.5368 735.57012 1.9825 20.68197 0.004753

Q1 1997 0.71727 0.00936 1.3588 715.32521 1.3768 11.94692 0.004499

Q2 1997 0.74188 0.01261 1.3874 697.38540 1.9163 9.31505 0.004807

Q3 1997 0.75666 0.01181 1.4753 745.38676 1.8453 12.93021 0.004861

Q4 1997 0.77381 0.01321 1.6129 626.65495 1.9142 10.71399 0.004893

Q1 1998 0.79496 0.01094 1.3203 820.39569 1.9497 14.56454 0.004609

Q2 1998 0.81361 0.01167 1.3713 843.90023 1.8783 14.45288 0.004852

Q3 1998 0.82424 0.01140 1.5054 541.36788 1.9264 14.47885 0.004979

Q4 1998 0.83764 0.01094 1.6424 523.82222 1.7847 15.41242 0.005035

Q1 1999 1.25572 0.01301 1.3072 400.40657 1.6392 14.37022 0.004784

Q2 1999 1.22996 0.01189 1.3464 485.82401 1.8855 13.88495 0.004956

Q3 1999 1.34461 0.01014 1.4311 495.33027 1.8043 16.12240 0.005019

Q4 1999 1.41431 0.01194 1.5693 421.94209 1.8682 13.63022 0.004875

Q1 2000 1.30765 0.01184 1.2664 447.41007 1.7634 14.13190 0.004743

Q2 2000 1.32810 0.01101 1.2856 320.35989 1.7955 15.04267 0.004854

Q3 2000 1.36467 0.01115 1.2885 353.57309 1.9692 14.02748 0.004865

Q4 2000 1.46515 0.01194 1.4377 370.71551 1.9210 13.42136 0.004760

Q1 2001 1.53326 0.00998 1.2137 381.56341 1.8649 14.80000 0.004564

Q2 2001 1.74858 0.01046 1.2331 409.66269 1.8963 14.06996 0.004788

Q3 2001 1.99114 0.01124 1.2435 438.95684 2.0350 13.21895 0.004872

Q4 2001 2.04774 0.01194 1.3842 396.62114 1.9123 13.87197 0.004953

Q1 2002 1.92180 0.01239 1.1935 401.30184 1.7731 12.28011 0.004584

Q2 2002 2.04156 0.01302 1.2078 456.17230 1.6294 13.09183 0.004874

Q3 2002 2.59124 0.00977 1.2103 414.35015 2.3584 11.63527 0.004920

Q4 2002 3.18668 0.01275 1.2900 408.69775 1.9016 12.02497 0.004778

Q1 2003 3.16355 0.01167 1.0300 449.09956 1.8936 12.03113 0.004728

Q2 2003 2.78503 0.01265 1.0376 510.10845 1.9313 11.66564 0.004909

Q3 2003 2.73695 0.01413 1.0627 555.76663 2.1118 11.23595 0.004927

Q4 2003 2.75371 0.01337 1.1925 522.65217 2.0751 13.12536 0.004869

Q1 2004 2.75271 0.01050 0.9877 538.15594 2.1294 11.89170 0.004896

Q2 2004 2.90157 0.01347 1.0283 513.01299 1.9101 12.61680 0.005132

Q3 2004 2.89416 0.01113 1.0458 509.29748 2.3665 11.96662 0.004985

Q4 2004 2.74692 0.01164 1.1581 543.53524 2.1288 13.12613 0.004903

Q1 2005 2.63879 0.01082 0.9676 626.32698 1.9494 10.75179 0.004867

Q2 2005 2.48613 0.01020 0.9808 601.61778 1.9574 11.59778 0.005099

Q3 2005 2.31059 0.01014 0.9963 560.33024 2.1562 11.13783 0.005018

Q4 2005 2.27687 0.00949 1.0967 534.16557 2.0210 11.42112 0.004968

Q1 2006 2.21922 0.00938 0.9542 585.07053 2.0085 12.32982 0.004965

Q2 2006 2.18842 0.00886 0.9662 603.53882 1.7757 14.47466 0.005146

Q3 2006 2.17852 0.00907 0.9818 706.79991 2.4143 10.65204 0.005116

Q4 2006 2.18956 0.01004 1.0784 831.16538 2.1978 12.23260 0.005057

Q1 2007 2.13465 0.00928 0.9700 1042.23582 2.1463 10.67825 0.005189

Q2 2007 1.99629 0.00862 0.9944 1379.56330 2.1196 11.71721 0.005398

Q3 2007 1.94595 0.00783 0.9865 1527.31080 2.4098 11.17238 0.005360

Q4 2007 1.81940 0.00794 1.0713 1677.67496 2.2746 11.53011 0.005324

Q1 2008 1.76910 0.00787 0.9874 1786.57084 2.0547 11.25630 0.005339

Q2 2008 1.67627 0.00669 0.9823 1793.37265 2.3277 12.74590 0.005510

Q3 2008 1.71478 0.00650 0.9878 1844.24768 2.3691 12.84016 0.005544

Q4 2008 2.46339 0.00725 1.0639 1801.15603 2.0304 10.61272 0.005235

Source: IFS/IMF - BLS - WTO/TRAINS - BDB


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Table 6:

Table 6: Data Russia (Levels)

Index 2005=100



Q1 1995 0.41388 0.01149 6.7514 84.55670 496.1516 9.56332 0.000909

Q2 1995 0.60687 0.01110 6.6346 159.04791 531.5217 10.40858 0.000943

Q3 1995 0.64383 0.00880 6.5742 173.73636 462.8715 10.31226 0.001094

Q4 1995 0.74508 0.00889 6.5332 219.03767 576.2959 11.40777 0.001001

Q1 1996 0.85156 0.01132 6.4289 241.55556 634.3090 10.89358 0.000895

Q2 1996 0.94293 0.01128 6.4175 197.97873 651.0993 11.41617 0.000917

Q3 1996 1.01039 0.01001 6.4242 186.67387 572.5550 10.81985 0.001036

Q4 1996 1.07929 0.01222 6.4241 188.79016 678.7929 11.21156 0.000969

Q1 1997 1.16399 0.01081 7.0620 201.44641 621.2802 12.62736 0.000886

Q2 1997 1.22579 0.00941 7.0595 299.24236 636.0501 14.22427 0.000901

Q3 1997 1.25134 0.00793 7.0559 280.00868 575.8225 13.54003 0.001055

Q4 1997 1.27857 0.00843 7.0569 215.43547 707.2175 14.52065 0.001088

Q1 1998 1.34265 0.01101 5.5680 203.09433 634.9215 13.90000 0.000951

Q2 1998 1.37948 0.01147 5.5672 193.82961 617.2071 13.54700 0.000968

Q3 1998 2.37912 0.01410 5.5260 151.79415 493.9360 10.73682 0.001056

Q4 1998 6.32705 0.02101 5.4512 145.72119 473.3147 9.68056 0.001010

Q1 1999 10.12615 0.01434 4.2457 127.48192 434.3075 12.60000 0.000950

Q2 1999 11.68216 0.01345 4.2392 142.88549 443.4398 13.97815 0.001002

Q3 1999 12.44628 0.01352 4.2383 130.48484 396.9873 11.82259 0.001172

Q4 1999 13.67147 0.01608 4.2399 144.61144 512.9625 14.39251 0.001114

Q1 2000 15.21691 0.01501 5.4473 177.27528 530.5736 11.10000 0.001045

Q2 2000 15.63435 0.01466 5.4519 237.97657 521.2406 11.54345 0.001076

Q3 2000 15.99226 0.01227 5.6597 281.31075 478.1430 11.90050 0.001249

Q4 2000 16.80793 0.01182 5.4554 314.12347 575.7840 13.34217 0.001174

Q1 2001 18.11800 0.01244 6.0431 329.46192 544.7206 10.25651 0.001068

Q2 2001 19.24234 0.01002 6.0407 384.79090 560.2940 12.36754 0.001107

Q3 2001 19.88410 0.00857 6.0438 415.97186 464.7662 9.97243 0.001305

Q4 2001 20.99536 0.00752 6.0438 404.97894 520.6845 12.10139 0.001214

Q1 2002 22.68000 0.01129 6.7831 407.57293 490.0275 9.81303 0.001089

Q2 2002 23.72823 0.01106 6.7945 473.33613 554.9702 12.49789 0.001146

Q3 2002 24.23601 0.00851 6.7976 492.48249 512.2390 11.31069 0.001343

Q4 2002 25.14702 0.00902 6.7959 516.23729 588.7275 9.81303 0.001285

Q1 2003 25.95755 0.01043 7.2111 589.01113 622.5955 12.48910 0.001180

Q2 2003 26.15470 0.00876 7.2773 685.33173 619.9212 14.22277 0.001246

Q3 2003 25.92951 0.00788 7.2775 654.92446 595.0280 13.43126 0.001413

Q4 2003 26.05604 0.00760 7.3483 815.72131 664.9282 14.23798 0.001405

Q1 2004 25.56010 0.01024 7.9972 869.58631 708.1535 7.90250 0.001260

Q2 2004 26.12283 0.00955 8.0082 908.77275 760.6722 9.11494 0.001337

Q3 2004 26.90394 0.00873 7.9950 978.35720 746.1801 8.58634 0.001508

Q4 2004 26.96275 0.00835 8.0829 1278.79083 862.1027 9.34728 0.001507

Q1 2005 27.22580 0.01013 8.8357 1388.73196 890.2903 9.65243 0.001301

Q2 2005 28.16221 0.00968 8.9215 1522.79791 961.4795 11.04835 0.001398

Q3 2005 28.30027 0.00846 9.0035 1568.30615 930.7828 10.64561 0.001580

Q4 2005 29.19754 0.00772 9.1376 1809.43577 1024.1371 11.25196 0.001584

Q1 2006 29.53440 0.00927 10.3427 2013.47866 1079.5493 6.49926 0.001370

Q2 2006 28.61912 0.00765 10.4812 2412.98776 1159.1503 8.17201 0.001499

Q3 2006 28.52936 0.00642 10.4918 2563.56807 1089.7356 7.13660 0.001682

Q4 2006 28.76797 0.00522 10.5223 2940.99658 1171.0552 7.92433 0.001697

Q1 2007 28.91470 0.00662 11.8065 3224.09239 1175.4279 9.00540 0.001456

Q2 2007 28.59923 0.00570 11.9462 3806.10975 1275.9421 11.10769 0.001573

Q3 2007 28.76989 0.00488 11.9085 3986.70827 1224.6934 9.95830 0.001756

Q4 2007 28.53793 0.00491 12.0287 4454.00195 1454.5742 10.74355 0.001808

Q1 2008 28.94765 0.00665 12.6399 4690.67158 1605.7469 8.77593 0.001572

Q2 2008 28.57836 0.00561 12.7827 5080.82417 1752.9423 10.99504 0.001672

Q3 2008 29.96104 0.00495 12.7827 4973.02168 1722.9889 9.64375 0.001850

Q4 2008 35.87728 0.00412 12.9151 3969.58091 1375.5261 7.75963 0.001838

Source: IFS/IMF - BLS - WTO/TRAINS - ROSSTAT


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Table 7:

Table 7: Data India (Levels)

Index 2005=100



Q1 1993 17.13240 0.02257 5.5783 131.96739 6.8442 47.80000 0.001707

Q2 1993 19.54613 0.02099 4.8721 139.05687 6.7125 48.78756 0.002053

Q3 1993 20.17732 0.01973 4.8256 153.11536 6.4411 49.54170 0.002147

Q4 1993 20.68107 0.01809 4.9668 181.24998 6.1750 49.23146 0.002187

Q1 1994 20.58851 0.02112 5.3911 249.92098 7.9325 47.80000 0.001817

Q2 1994 21.06922 0.01997 4.8628 264.66309 6.9324 49.18285 0.002089

Q3 1994 21.76507 0.01845 4.7824 295.35351 7.3047 49.84531 0.002168

Q4 1994 22.13777 0.01765 5.0257 300.90634 7.6681 48.69658 0.002113

Q1 1995 22.04864 0.01994 5.8530 320.11915 9.2485 41.00000 0.001940

Q2 1995 22.55126 0.01917 5.3738 304.05521 8.8928 42.31501 0.002018

Q3 1995 24.04394 0.01706 5.2721 292.02198 8.7786 43.00228 0.002177

Q4 1995 26.32944 0.01570 5.4533 274.84875 9.2973 41.52059 0.002340

Q1 1996 26.40067 0.01775 6.5690 266.63215 11.4843 37.00000 0.002064

Q2 1996 26.53523 0.01591 5.8922 271.07417 9.6326 38.36315 0.002435

Q3 1996 28.16466 0.01435 5.6402 278.71792 8.7038 38.54013 0.002662

Q4 1996 28.61503 0.01489 5.6628 293.02337 9.5395 37.68519 0.002475

Q1 1997 28.63853 0.01700 5.6908 318.13172 12.6195 30.09231 0.002030

Q2 1997 28.80544 0.01893 5.1067 353.53150 12.6028 30.37864 0.001846

Q3 1997 29.27560 0.01508 6.2559 353.30175 10.3143 30.57446 0.002401

Q4 1997 31.19020 0.01436 6.3441 333.96392 10.7141 30.79018 0.002433

Q1 1998 33.70567 0.01556 6.3871 346.43733 13.1620 30.22866 0.002131

Q2 1998 35.58121 0.01662 5.9056 320.90398 13.1611 30.88808 0.002130

Q3 1998 39.35895 0.01296 6.8108 345.11617 12.0064 32.11311 0.002642

Q4 1998 40.89112 0.01226 6.6651 355.66472 11.1226 31.58944 0.002616

Q1 1999 39.06565 0.01406 6.9347 386.18856 12.7965 32.94551 0.002278

Q2 1999 39.31341 0.01514 6.4617 394.08488 13.5621 33.07742 0.002246

Q3 1999 40.20066 0.01222 7.7880 387.89391 12.6690 33.60231 0.002738

Q4 1999 40.96787 0.01190 7.7797 407.16275 12.7745 33.64100 0.002626

Q1 2000 40.06226 0.01348 7.2556 435.41865 14.9192 33.65109 0.002414

Q2 2000 41.03127 0.01432 6.7564 417.59870 15.8946 34.30075 0.002507

Q3 2000 42.35507 0.01203 8.0220 399.75971 14.0661 33.95698 0.002970

Q4 2000 43.79927 0.01223 7.9231 450.93087 14.4660 33.97953 0.002843

Q1 2001 42.80296 0.01351 7.3265 470.04468 15.5893 32.32250 0.002524

Q2 2001 43.41874 0.01399 6.9128 478.21592 16.1113 32.88022 0.002577

Q3 2001 44.94217 0.01130 8.1513 491.51067 13.8943 33.22826 0.003122

Q4 2001 46.32482 0.01128 8.0682 533.00498 13.4874 32.55437 0.003002

Q1 2002 46.26434 0.01239 7.2260 591.93097 16.0293 29.00364 0.002714

Q2 2002 46.86196 0.01297 6.8545 630.25580 17.6082 29.33486 0.002761

Q3 2002 47.30946 0.01097 7.8960 679.55359 16.2169 29.67908 0.003171

Q4 2002 47.36373 0.01069 8.0119 760.18118 15.8866 29.20367 0.003120

Q1 2003 45.82296 0.01184 7.4155 802.14490 18.2355 28.78789 0.002724

Q2 2003 46.19658 0.01187 7.2393 878.14451 18.0438 29.38187 0.002892

Q3 2003 45.24673 0.01004 8.4359 964.99968 15.6985 29.07886 0.003289

Q4 2003 45.26936 0.01023 8.2664 1084.19667 18.4798 28.97994 0.003102

Q1 2004 44.36294 0.01114 7.7203 1171.25669 21.4669 29.93625 0.002716

Q2 2004 43.82941 0.01145 7.5454 1222.35668 20.9185 30.17382 0.002862

Q3 2004 46.13737 0.00972 8.6914 1222.24151 20.6430 30.68270 0.003226

Q4 2004 45.13379 0.00969 8.6448 1338.95771 22.6245 30.12791 0.003103

Q1 2005 43.30390 0.00830 7.2246 1421.00652 28.3772 18.86237 0.002766

Q2 2005 43.17981 0.00946 9.1634 1380.12085 28.6802 19.34230 0.002829

Q3 2005 43.26502 0.00786 9.7218 1397.65276 25.3037 20.21165 0.003250

Q4 2005 46.27750 0.00893 11.1033 1350.58057 25.2351 16.78089 0.003198

Q1 2006 44.47752 0.01047 10.5556 1472.40717 30.5167 15.04521 0.002782

Q2 2006 45.73167 0.00907 8.1139 1566.18445 31.5790 19.12312 0.002828

Q3 2006 47.78945 0.00982 9.5552 1581.02137 31.0932 12.59414 0.003208

Q4 2006 47.61351 0.00887 11.7343 1705.19790 29.0119 16.09259 0.003163

Q1 2007 46.06576 0.00717 10.1878 1883.47095 31.5829 17.07948 0.002693

Q2 2007 42.93769 0.00890 8.9019 1990.28619 34.3124 13.96870 0.002799

Q3 2007 43.36972 0.00912 9.5834 2309.43859 29.6275 14.50278 0.003200

Q4 2007 42.34276 0.00834 13.6103 2547.75696 30.3591 17.74412 0.003157

Q1 2008 42.44690 0.01246 11.3647 2817.43432 38.3528 12.43390 0.002740

Q2 2008 44.50414 0.01290 9.7274 2771.20510 44.1334 12.77078 0.002858

Q3 2008 48.68682 0.01140 12.0624 2539.99860 43.0793 12.94075 0.003230

Q4 2008 57.60750 0.01128 16.0789 2361.07162 37.5740 12.69173 0.003106

Source: IFS/IMF - BLS - WTO/TRAINS


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Table 8:

Table 8: Data China (Levels)

Index 2005=100


NER*CPI/CPIUSA (UVX/UVI)/GDP PE/CPI Foreign assets/CPIUSA (M+X)/GDP Applied MFN (GDP/Nº of Em.).

Q1 1993 8.63979 0.03126 9.3575 313.04552 2.1621 39.87482 0.050238

Q2 1993 8.76305 0.02983 9.6231 304.25273 2.3892 40.17561 0.042738

Q3 1993 8.84754 0.02719 9.7298 338.36260 2.3305 40.18674 0.047284

Q4 1993 9.07422 0.02694 9.5964 358.28015 2.3151 39.60401 0.048215

Q1 1994 13.90274 0.02739 9.8871 371.84898 2.0647 36.34620 0.055901

Q2 1994 13.80349 0.02646 10.4360 469.14532 2.4191 36.62805 0.055911

Q3 1994 14.06345 0.02452 10.1651 581.13045 2.3981 37.05760 0.053259

Q4 1994 13.70845 0.02396 10.3948 682.71212 2.3990 36.82651 0.044235

Q1 1995 12.98442 0.02419 11.3646 763.94292 2.2290 22.40000 0.061457

Q2 1995 12.43080 0.02342 12.0536 817.03633 2.4878 22.89574 0.064117

Q3 1995 11.79629 0.02230 12.9482 924.11489 2.5014 22.56828 0.077780

Q4 1995 11.45905 0.02197 13.2633 1021.06642 2.3737 22.14602 0.072833

Q1 1996 11.27362 0.02196 13.8268 1105.52620 2.1287 23.65498 0.066743

Q2 1996 11.08907 0.02135 14.4929 1259.24735 2.3054 23.45448 0.074673

Q3 1996 10.86638 0.02015 15.0975 1300.80952 2.3184 23.53208 0.088405

Q4 1996 10.75825 0.01955 15.1643 1419.49054 2.2128 23.60432 0.078601

Q1 1997 10.35959 0.02032 16.2436 1613.51339 2.0506 17.60723 0.072030

Q2 1997 10.21916 0.01939 17.3113 1724.36413 2.2184 17.50343 0.074015

Q3 1997 10.05799 0.01842 16.9085 1877.52911 2.2393 17.57971 0.091546

Q4 1997 9.90427 0.01885 17.0272 1967.75613 2.3005 17.43083 0.086549

Q1 1998 9.87592 0.01912 17.8814 1944.24381 1.8982 17.47821 0.076774

Q2 1998 9.63247 0.01829 17.4526 1931.14334 2.0368 17.22967 0.089123

Q3 1998 9.57800 0.01819 18.4882 1940.25580 1.9873 17.26622 0.096928

Q4 1998 9.60698 0.01829 18.0598 1981.98877 1.9631 17.39115 0.081101

Q1 1999 9.46682 0.01765 19.5486 1981.57158 1.6290 17.10891 0.081757

Q2 1999 9.36959 0.01729 29.6732 1971.23133 1.8300 16.90807 0.120515

Q3 1999 9.39660 0.01617 32.1071 2003.84120 1.9118 17.24820 0.122719

Q4 1999 9.35636 0.01493 56.8180 2083.74465 1.8220 17.32795 0.158789

Q1 2000 9.27257 0.01614 24.6755 2074.76753 1.8282 16.94782 0.087789

Q2 2000 9.27212 0.01553 35.6679 2075.05804 1.9673 16.95348 0.079451

Q3 2000 9.15840 0.01410 37.1037 2064.10447 2.0003 17.01006 0.084293

Q4 2000 9.27765 0.01386 64.9828 2114.04267 1.9242 16.90272 0.089488

Q1 2001 9.09835 0.01498 29.1005 2124.53999 1.7659 15.89049 0.093855

Q2 2001 9.05966 0.01452 42.6393 2259.62176 1.8083 15.72881 0.099487

Q3 2001 8.91037 0.01366 45.4567 2418.21437 1.8500 15.79250 0.133734

Q4 2001 8.97301 0.01346 75.9031 2653.45005 1.6738 15.64399 0.138436

Q1 2002 8.82309 0.01396 36.4718 2595.27330 1.5640 12.37561 0.101392

Q2 2002 8.76944 0.01332 49.2458 2660.91287 1.7217 12.30936 0.091700

Q3 2002 8.72466 0.01242 53.5310 2794.45566 1.8452 12.41557 0.093279

Q4 2002 8.75618 0.01158 87.4657 3033.16845 1.7019 12.34905 0.089790

Q1 2003 8.71325 0.01259 39.5040 3232.50866 1.6896 11.25259 0.110519

Q2 2003 8.68669 0.01332 58.7391 3489.25394 1.9564 11.16817 0.118134

Q3 2003 8.68254 0.01094 56.4998 3817.04423 1.7875 11.22828 0.130805

Q4 2003 8.90419 0.01016 92.3499 3989.10279 1.7332 11.30129 0.131203

Q1 2004 8.74435 0.01127 44.5731 4280.72974 1.7690 10.41936 0.120421

Q2 2004 8.79992 0.01074 37.7040 4532.31179 1.9113 10.50803 0.118624

Q3 2004 8.80892 0.00982 39.6481 4904.85692 1.8809 10.57323 0.143529

Q4 2004 8.55702 0.00930 41.7582 5825.96056 1.7806 10.48434 0.159872

Q1 2005 8.44807 0.01003 41.9457 6281.75635 1.7095 10.66558 0.131883

Q2 2005 8.30683 0.00953 37.4699 6793.03541 1.8855 10.68741 0.122297

Q3 2005 7.93921 0.00891 40.0385 7258.53275 1.9266 10.70917 0.132711

Q4 2005 8.01787 0.00819 39.9256 7792.65798 1.7638 10.66558 0.142318

Q1 2006 7.80382 0.00886 43.7729 8314.31495 1.7493 9.67584 0.146140

Q2 2006 7.70193 0.00866 37.4424 8827.89992 1.8489 9.78364 0.104837

Q3 2006 7.65738 0.00834 39.9400 9453.14712 1.9901 9.79216 0.120788

Q4 2006 7.69864 0.00735 47.6779 10560.51927 1.7841 9.67584 0.150568

Q1 2007 7.50131 0.00784 52.1811 11762.56199 1.6734 9.77966 0.126226

Q2 2007 7.39146 0.00739 46.1728 12810.59372 1.7635 9.81830 0.121023

Q3 2007 7.39876 0.00712 47.7575 14363.74606 1.8425 9.88541 0.126558

Q4 2007 7.24059 0.00647 49.3080 15625.30299 1.7061 9.87476 0.143957

Q1 2008 6.98014 0.00642 52.1312 17558.89729 1.5110 9.42562 0.151179

Q2 2008 6.54328 0.00639 49.0127 19197.83162 1.6239 9.57838 0.159608

Q3 2008 6.28323 0.00663 52.3829 20618.99447 1.7718 9.60240 0.188459

Q4 2008 6.32115 0.00701 55.9348 22106.13467 1.7536 9.33018 0.363619

Source: IFS/IMF - BLS - WTO/TRAINS - Reuters


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Table 9:

Table 9: Brazil. Real exchange rate (RER) and its real determinants (1993 - 2008)

Unit root test (Augmented Dickey - Fuller)

MacKinnon (1996) one-sided p-values

No trend Intercept Trend and No trend Intercept Trend and

no intercept Intercept no intercept Intercept

Real exchange rate: Order of integration = 1

RER in levels 0.773 -1.323 -2.398 RER in 1st differences -4.757 -5.076 -4.996

1% level -2.603 -3.542 -4.113 1% level -2.603 -3.542 -4.116

5% level -1.946 -2.910 -3.484 5% level -1.946 -2.910 -3.485

10% level -1.613 -2.593 -3.170 10% level -1.613 -2.593 -3.171

SIC (maxlag=9) 2 2 1 SIC (maxlag=9) 1 1 1

Structural Break No No No

Chow p-value F 0.573 0.799 0.791

Terms of trade: Order of integration = 1

TT in levels 1.969 -1.771 -2.426 TT in 1st differences -12.062 -7.316 -7.290

1% level -2.603 -3.540 -4.113 1% level -2.603 -3.542 -4.116

5% level -1.946 -2.909 -3.484 5% level -1.946 -2.910 -3.485

10% level -1.613 -2.592 -3.170 10% level -1.613 -2.593 -3.171


Structural Break No at 1 and 5% No No

Chow p-value F 0.091 0.408 0.162

Public Expenditure: Order of integration = 0

PE in levels -4.197 -3.614 -9.405

1% level -2.607 -3.546 -4.121

5% level -1.947 -2.912 -3.488

10% level -1.613 -2.594 -3.172

SIC (maxlag=9) 7 4 4

Net Foreign Assets: Order of integration = 1

NFA in levels 2.495 1.284 0.357 NFA in 1st differences -5.439 -5.665 -5.881

1% level -2.602 -3.538 -4.110 1% level -2.603 -3.540 -4.113

5% level -1.946 -2.908 -3.483 5% level -1.946 -2.909 -3.484

10% level -1.613 -2.592 -3.169 10% level -1.613 -2.592 -3.170



Chow p-value F 0.846 0.650 0.444

Balance of Trade: Order of integration = 1

BT in levels 0.930 -0.683 -8.530 BT in 1st differences -11.155 -11.192 -11.164

1% level -2.604 -3.544 -4.110 1% level -2.604 -3.544 -4.118

5% level -1.946 -2.911 -3.483 5% level -1.946 -2.911 -3.487

10% level -1.613 -2.593 -3.169 10% level -1.613 -2.593 -3.172



Chow p-value F 0.949 0.869 0.287

Tariffs: Order of integration = 1

TAR in levels -0.680 -3.510 -4.481 TAR in 1st differences -10.430 -10.352 -10.270

1% level -2.603 -3.538 -4.110 1% level -2.603 -3.540 -4.113

5% level -1.946 -2.908 -3.483 5% level -1.946 -2.909 -3.484

10% level -1.613 -2.592 -3.169 10% level -1.613 -2.592 -3.170

SIC (maxlag=9) 1 0 0 SIC (maxlag=9)

Structural Break No No No at 1 and 5%

Chow p-value F 0.675 0.432 0.054

Productivity: Order of integration = 1

PR in levels 1.104 -0.815 -2.285 PR in 1st differences -3.407 -3.599 -4.973

1% level -2.605 -3.546 -4.121 1% level -2.605 -3.546 -4.131

5% level -1.946 -2.912 -3.488 5% level -1.946 -2.912 -3.492

10% level -1.613 -2.594 -3.172 10% level -1.613 -2.594 -3.175


Structural Break No No at 1 and 5% No at 1 and 5%

Chow p-value F 0.455 0.058 0.058


Note: RER, PE, NFA, BT, TAR and PR in levels. TT in logarithms

Breaking point: 1995q1. SIC models maxlag ? 3: 1995q4. PE and PR with trend and intercept: 1996q1.


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Table 10:

Table 10: Russia. Real exchange rate (RER) and its real determinants (1995 - 2008)






RER in levels 1.902 0.032 -2.637 RER in 1st differences -0.846 -2.916 -2.709

1% level -2.609 -3.560 -4.145 1% level -2.610 -3.560 -4.141

5% level -1.947 -2.918 -3.499 5% level -1.947 -2.918 -3.497

10% level -1.613 -2.597 -3.179 10% level -1.613 -2.597 -3.178


Structural Break No No No Structural Break No No No

Chow p-value F 0.915 0.774 0.216 Chow p-value F 0.8364 0.8704 0.1658

RER in 2nd differences -5.333 -5.322 -5.291

1% level -2.610 -3.563 -4.145

5% level -1.947 -2.919 -3.499

10% level -1.613 -2.597 -3.179



TT in levels 0.693 -1.303 -2.610 TT in 1st differences -7.554 -7.593 -7.814

1% level -2.608 -3.555 -4.134 1% level -2.609 -3.560 -4.141

5% level -1.947 -2.916 -3.494 5% level -1.947 -2.918 -3.497

10% level -1.613 -2.596 -3.176 10% level -1.613 -2.597 -3.178


Structural Break No No at 1% No at 1 and 5%

Chow p-value F 0.300 0.028 0.078


PE in levels 2.120 1.063 -1.328 PE in 1st differences -6.724 -7.099 -7.783

1% level -2.608 -3.555 -4.134 1% level -2.608 -3.557 -4.137

5% level -1.947 -2.916 -3.494 5% level -1.947 -2.917 -3.495

10% level -1.613 -2.596 -3.176 10% level -1.613 -2.596 -3.177



Chow p-value F 0.290 0.326 0.177


NFA in levels -5.794 -6.518 -6.497

1% level -2.611 -3.565 -4.148

5% level -1.947 -2.920 -3.500

10% level -1.613 -2.598 -3.180



BT in levels 1.793 0.865 -0.579 BT in 1st differences -4.382 -4.653 -5.136

1% level -2.610 -3.563 -4.145 1% level -2.610 -3.563 -4.145

5% level -1.947 -2.919 -3.499 5% level -1.947 -2.919 -3.499

10% level -1.613 -2.597 -3.179 10% level -1.613 -2.597 -3.179


Structural Break No at 1 and 5% No at 1 and 5% Si

Chow p-value F 0.065 0.052 0.001



1% level -2.608 -3.555 -4.134 1% level -2.608 -3.557 -4.137

5% level -1.947 -2.916 -3.494 5% level -1.947 -2.917 -3.495

10% level -1.613 -2.596 -3.176 10% level -1.613 -2.596 -3.177


Structural Break No No at 1% No

Chow p-value F 0.972 0.040 0.828


PR in levels 3.907 1.930 -2.324 PR in 1st differences -1.658 -2.940 -4.660

1% level -2.613 -3.571 -4.157 1% level -2.611 -3.565 -4.157

5% level -1.948 -2.922 -3.504 5% level -1.947 -2.920 -3.504

10% level -1.613 -2.599 -3.182 10% level -1.613 -2.598 -3.182


Structural Break No No No Structural Break No No No

Chow p-value F 0.915 0.926 0.933 Chow p-value F 0.6621 0.7463 0.9318

PR in 2nd differences -5.039 -5.000 -5.065

1% level -2.615 -3.578 -4.166

5% level -1.948 -2.925 -3.509

10% level -1.612 -2.601 -3.184



Note: RER, PE, NFA, BT, TAR y PR in levels. TT in logarithms

Breaking point: 2005q2.


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Table 11:

Table 11: India. Real exchange rate (RER) and its real determinants (1993 - 2008)






RER in levels 1.588 -1.674 -2.004 RER in 1st differences -3.110 -3.560 -3.345

1% level -2.603 -3.550 -4.113 1% level -2.603 -3.540 -4.113

5% level -1.946 -2.914 -3.484 5% level -1.946 -2.909 -3.484

10% level -1.613 -2.595 -3.170 10% level -1.613 -2.592 -3.170



Chow p-value F 0.888 0.841 0.885


TT in levels 0.231 -1.829 0.733 TT in 1st differences -1.314 -1.183 -6.279

1% level -2.608 -3.557 -4.131 1% level -2.608 -3.557 -4.131

5% level -1.947 -2.917 -3.492 5% level -1.947 -2.917 -3.492

10% level -1.613 -2.596 -3.175 10% level -1.613 -2.596 -3.175



Chow p-value F 0.738 0.539 0.516


PE in levels 4.683 2.634 0.614 PE in 1st differences -3.403 -11.551 -12.332

1% level -2.604 -3.544 -4.118 1% level -2.605 -3.544 -4.118

5% level -1.946 -2.911 -3.487 5% level -1.946 -2.911 -3.487

10% level -1.613 -2.593 -3.172 10% level -1.613 -2.593 -3.172



Chow p-value F 0.930 0.921 0.871


NFA in levels 0.282 -0.720 -2.323 NFA in 1st differences -3.122 -3.324 -3.235

1% level -2.603 -3.540 -4.113 1% level -2.603 -3.540 -4.113

5% level -1.946 -2.909 -3.484 5% level -1.946 -2.909 -3.484

10% level -1.613 -2.592 -3.170 10% level -1.613 -2.592 -3.170



Chow p-value F 0.886 0.928 0.692


BT in levels 4.844 2.596 0.175 BT in 1st differences 0.183 -12.196 -13.091

1% level -2.603 -3.542 -4.116 1% level -2.608 -3.542 -4.116

5% level -1.946 -2.910 -3.485 5% level -1.947 -2.910 -3.485

10% level -1.613 -2.593 -3.171 10% level -1.613 -2.593 -3.171



Chow p-value F 0.402 0.567 0.612



1% level -2.602 -3.538 -4.110 1% level -2.603 -3.540 -4.113

5% level -1.946 -2.908 -3.483 5% level -1.946 -2.909 -3.484

10% level -1.613 -2.592 -3.169 10% level -1.613 -2.592 -3.170



Chow p-value F 0.219 0.201 0.278


PR in levels 2.110 -1.731 -1.720 PR in 1st differences -3.545 -4.932 -5.277

1% level -2.606 -3.550 -4.121 1% level -2.605 -3.550 -4.127

5% level -1.947 -2.914 -3.488 5% level -1.946 -2.914 -3.491

10% level -1.613 -2.595 -3.172 10% level -1.613 -2.595 -3.174


Structural Break No at 1 and 5% No at 1% No

Chow p-value F 0.052 0.014 0.150



Breaking point: 1995q1. SIC models maxlag ? 3: 1996q4. TT: 1998q3.


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Table 12:

Table 12: China. Real exchange rate (RER) and its real determinants (1993 - 2008)






RER in levels -0.641 -1.423 -3.636 RER in 1st differences -3.943 -7.106 -7.212

1% level -2.603 -3.542 -4.110 1% level -2.604 -3.540 -4.113

5% level -1.946 -2.910 -3.483 5% level -1.946 -2.909 -3.484

10% level -1.613 -2.593 -3.169 10% level -1.613 -2.592 -3.170



Chow p-value F 0.993 0.118 0.476


TT in levels 7.315 0.304 -1.390 TT in 1st differences -1.899 -9.711 -9.638

1% level -2.604 -3.544 -4.118 1% level -2.605 -3.544 -4.118

5% level -1.946 -2.911 -3.487 5% level -1.946 -2.911 -3.487

10% level -1.613 -2.593 -3.172 10% level -1.613 -2.593 -3.172


Structural Break No No No at 1%

Chow p-value F 0.444 0.678 0.040


PE in levels 0.642 -1.155 -1.987 PE in 1st differences -4.377 -4.600 -4.580

1% level -2.605 -3.546 -4.124 1% level -2.605 -3.546 -4.121

5% level -1.946 -2.912 -3.489 5% level -1.946 -2.912 -3.488

10% level -1.613 -2.594 -3.173 10% level -1.613 -2.594 -3.172


Structural Break No No No at 1 and 5%

Chow p-value F 0.436 0.144 0.088


NFA in levels 9.194 8.948 7.150

1% level -2.603 -3.540 -4.113

5% level -1.946 -2.909 -3.484

10% level -1.613 -2.592 -3.170



BT in levels -2.178 -1.639 -2.125 BT in 1st differences -3.347 -4.693 -4.823

1% level -2.607 -3.553 -4.121 1% level -2.605 -3.553 -4.131

5% level -1.947 -2.915 -3.488 5% level -1.946 -2.915 -3.492

10% level -1.613 -2.595 -3.172 10% level -1.613 -2.595 -3.175


Structural Break No No No at 1%

Chow p-value F 0.848 0.652 0.030



1% level -2.608 -3.555 -4.134 1% level -2.608 -3.555 -4.134

5% level -1.947 -2.916 -3.494 5% level -1.947 -2.916 -3.494

10% level -1.613 -2.596 -3.176 10% level -1.613 -2.596 -3.176



Chow p-value F 0.480 0.132 0.123


PR in levels 1.645 0.972 -0.869 PR in 1st differences -4.101 -4.260 -4.379

1% level -2.602 -3.538 -4.110 1% level -2.603 -3.540 -4.113

5% level -1.946 -2.908 -3.483 5% level -1.946 -2.909 -3.484

10% level -1.613 -2.592 -3.169 10% level -1.613 -2.592 -3.170


Structural Break No No at 1 and 5% No at 1 and 5%

Chow p-value F 0.220 0.056 0.074



Breaking point: 2001q1.


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Table 17:

Brazil. Estimated Cointegrated Vectors in Johansen Estimation

Cointegration with unrestricted intercepts and restricted trends in the VAR

*******************************************************************************

63 observations from 1993Q2 to 2008Q4. Order of VAR = 1, chosen r =2.


RER TT NFA BT TAR PR Trend

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Vector 1 Vector 2

RER .015007 .10323

( -1.0000) ( -1.0000)

TT .24669 -.25241

( -16.4388) ( 2.4450)

NFA .1309E-3 -.1886E-3

(-.0087201) ( .0018271)

BC -.82819 -.23788

( 55.1881) ( 2.3043)

TAR .0089794 -.027366

( -.59836) ( .26509)

PR 43.4808 926.4040

( -2897.4) ( -8973.9)

Trend .0072040 -.014051

( -.48006) ( .13611)

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Table 18:

Russia. Estimated Cointegrated Vectors in Johansen Estimation


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Vector 1 Vector 2 Vector 3

RER -.039532 .0053003 .11475

( -1.0000) ( -1.0000) ( -1.0000)

TT .067535 .87211 -.12421

( 1.7084) (-164.5407) ( 1.0825)

PE .0060780 .22851 .068509

( .15375) ( -43.1131) ( -.59704)

BT -.8570E-4 -.6825E-3 -.5189E-3

(-.0021679) ( .12877) ( .0045220)

TAR .0021087 .017660 -.0082495

( .053341) ( -3.3318) ( .071892)

PR -1115.0 437.5683 -90.9423

( -28205.4) ( -82555.6) ( 792.5366)

Trend .0023083 -.0022578 -.0069608

( .058391) ( .42597) ( .060662)

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Table 19:

India. Estimated Cointegrated Vectors in Johansen Estimation


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RER TT PE NFA BT TAR PR Trend

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Vector 1 Vector 2 Vector 3 Vector 4

RER -.0042334 .0065527 .021010 .0016895

( -1.0000) ( -1.0000) ( -1.0000) ( -1.0000)

TT -.36517 .31603 -1.1666 .40325

( -86.2591) ( -48.2293) ( 55.5271) (-238.6794)

PE .042004 .022631 -.083390 -.12551

( 9.9221) ( -3.4537) ( 3.9690) ( 74.2894)

NFA .3842E-3 .6172E-4 .2455E-3 .4037E-3

( .090759) (-.0094188) ( -.011685) ( -.23897)

BT -.049971 .017509 .025528 -.020329

( -11.8040) ( -2.6720) ( -1.2150) ( 12.0326)

TAR -.0043343 -.012444 .0072467 -.011781

( -1.0238) ( 1.8990) ( -.34491) ( 6.9729)

PR -167.2600 698.1220 65.0128 217.0217

( -39510.0) (-106538.9) ( -3094.3) (-128453.9)

Trend .0065519 -.031647 -.036149 .0053966

( 1.5477) ( 4.8296) ( 1.7205) ( -3.1942)

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ISSN: 2226-6348

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Table 20:

China. Estimated Cointegrated Vectors in Johansen Estimation


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Vector 1 Vector 2

RER -.014790 -.041091

( -1.0000) ( -1.0000)

TT -1.6786 1.2610

(-113.4953) ( 30.6882)

PE .0057178 .0039894

( .38659) ( .097087)

BT .081376 .97740

( 5.5019) ( 23.7864)

TAR -.0080277 -.0021260

( -.54276) ( -.051740)

PR 1.8359 .0066545

( 124.1248) ( .16195)

Trend -.050473 .034552

( -3.4126) ( .84086)

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