Finance, Volatility and Growth

8/18/2019 Finance, Volatility and Growth

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Finance, Volatility and Growth:

Non-Linear Time-Series Evidence for Brazil since 1870

Nauro F. Camposa,b

Menelaos Karanasos a Jihui Zhang

a

aDepartment of Economics and Finance, Brunel University (UK)

bCEPR and IZA

December 2010

Abstract

This paper uses the power-ARCH (PARCH) framework with annual time series data

to evaluate the main explanations for the remarkable economic performance of Brazil

from 1870 to 2003. The emphasis is on the role of the domestic financial development,

international financial factors, trade openness, and public deficits. The major findings

are as follows: (1) financial development and trade openness both show a strong


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1. Introduction

The association between financial development and economic growth was first raised

by Schumpeter (1912). For the first time, he emphasizes the importance of finance to

the growth and development of a capitalist economy. Subsequently, studies including

King and Levine (1993), Levine and Zervos (1998) and Beck and Levine (2004),

using differing samples of countries, report that measures of financial development

have a positive effect on long-run growth.

Still, the majority of the empirical research relies on cross-country studies, although

employing various methodologies to try to take the potential simultaneity issue into

account (Adrogue et al. 2006; Castelar et al. 2005). Relatively, little attention has been

devoted to the use of time series technique and single country analysis.

Given this background, this paper investigates the role of the financial development,

trade openness, public deficit in generating growth by using a power-ARCH (PARCH)

framework with annual time series data for Brazil, covering the period from 1870 to

2003.

Therefore, this paper contributes to the debate within three particular areas：First,

we answer the question of what is the relationship between financial development and


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we studied only one particular country over a very long period of time with annual

frequency data to exam the impact of financial development on the output growth.

The second area of focus is to explain the association of volatility and growth.

Ramey and Ramey (1995) point out that output growth rate are negatively associated

with their volatility, while Grier and Tullock (1989) report that higher standard

deviations of growth are related with higher mean rates. Further, as to Brazil, more

papers have concentrated on inflation uncertainty and growth. Though it is a hot

debate, there is no general agreement on recent papers. Dotsey and Sarte (2000) argue

that in the long term inflation uncertainty adversely affects long term growth but the

effect can be contrary in short term. They claimed that it could be a precautionary

savings motivation which boosted the inflation uncertainty to the growth. However,

Issler et al. (1998) show that precautionary savings in Brazil is not a significant factor.

Thirdly, except of testing individual univariate effects on the economic growth, we

also use multivariate analysis to help to explain the association. In doing so, for each

effect we report estimates of both single variable test and multivariate results. Besides,

considering the development of global financial market, this paper also tries to shed

some lights on the field of whether development of international financial


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financial development affect Brazil’s economy growth positively in the long – term,

however, this positive effect associates with a negative short – term relationship. 2)

Empirical results also suggested that public deficit do affect growth via volatility

channel. 3) Global financial development links with Brazil’s economy – positively in

the short run while negatively in the short run.

The paper is organized as follows: section 2 describes the data including data

resources and definitions of each indicator; section 3 provides details of our

econometric methodology. Section 4 reports our main results and findings and section

5 concludes and suggests the direction of our future research.

2. Brief Background on Economic Growth in Brazil since 1870

Among economic historians, and at least until 1980, Brazil is widely considered to

be one of the fastest growing economies in the world (Maddison, 1995). Yet, for the

following two decades, due to the banking and currency crises, price instability and

high protection against imports lead to GDP growth not only much lower than other

developing countries but also against to the previous 50 years. After the period of

1960 – 1980, the disappointing growth period, Brazil’s economy has improved in


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However, in relative previous studies of Brazil, there is little attention has been

given to the period of 1870 to the end of the World War I, especially in the field of the

association between financial development and output growth. Nevertheless, since the

early nineteenth century, Brazil declared its independence and also built up its first

modern style financial system1, a basic cycle between financial development and

output growth has been built. Besides, a part from the financial development, it has

been well known that trade openness, consist the major proportion of the economic

growth in Brazil, boosted the growth, to specify – coffee exportation. As Werner

Baear (2001) states, there is no doubt that coffee exports were the engine of growth

throughout most of the nineteenth century2. Thus, whether financial development

together with other indicators will affect Brazil’s output growth interested us the most

in this period – from 1870 to 1930.

Next, one of the most important contributions to the study of the long – term

Brazilian economic growth from 1930 – 1990 was given by Abreu and Verner (1997).

They studied various fields including financial development, degree of the openness,

education policies and etc. But, in their findings we cannot see strong evidence of

financial development boosted growth. As they argued: “increased public sector


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whether the relationship between financial development and output growth in Brazil

obey the economic theory, our results present a different story. By using a different

econometric approach, we find that financial development affects the long-term

growth positively while associate with a negative short-run effect.

Finally, from 1990 till early 21st century, the growth of Brazil becomes a hot

debated issue. A lot of recent researches on either Latin America or Brazil covered

this period to study the role of financial development. Bittencourt (2010) found that

financial development played a significant role in generating growth in Latin America.

Castelar et al (2005) examined the relationships between financial development

growth and equity. Also, Stefani (2007) investigated this relationship in Brazil

between 1980 till 2006 by using a cointegration model. Further, some more papers

shed some light on the relative fields like how interest rates and inflations affect

Brazil’s recent growth (Muinhos and Nakane 2006; Vale 2002.). In line with the

economy theory most of the papers obtained a strong positive relationship between

financial development and output growth in Brazil.

To sum up, most historical researches of Brazil divided the examining period in to

several parts. Indeed, Brazil has it’s particularly tendency of growth rate mixtures


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development of the global financial market in generating the economic growth in

Brazil.

3. Data

The main data are from “International Historical Statistics: The Americas: 1750 –

2000” (Mitchell. B. R., 2003). Data was record yearly for Brazil including: Gross

Domestic Product, Saving Bank Deposits, Deposits in Commercial Banks, M1, and

M2. However, the money standards of the data changed from time to time and figures

are often incomplete for the given year. Therefore, in order to find relatively complete

series to avoid bias as much as possible, other resources are included.

Various measures of financial development are used. One is the ratio of M1 to GDP.

The narrow money divided by growth capture the financial depth or the relative size

of the financial system. Besides in order to capture the efficiency of the financial

sector, two other measures of financial development are also used. Deposits in

Commercial Banks have been reported by Mitchell. B. R. (2003). However, due to the

missing figures, we follow a more practicable method of Peláez and Suzigan (1976) to

regenerate the series. Total deposits in Commercial Banks are defined as the


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Mitchell (2007) and IBGE3. A public deficit is provided as the ratio of total public

deficits to GDP, while trade openness is measured as the ratio of imports plus exports

then divided by the GDP.

At last, international financial sector developments should also have impact on

Brazil’s economic growth, although for most of the period since 1930 Brazil remained

a closed economy. Marcelo Abreu states from 1930-1980 Brazil had a “cross-eyed”

foreign economic orientation, with bold export promotion polices and a rather closed

domestic market. But Brazil, as the largest economy in Latin America, and ninth

largest in the world, cannot be isolated to the world economy environment. However,

it is still hard to measure the world economy environment itself, especially when we

take both the depression and World War period into account.

Thus, in standard fashion in this type of study, we use the level of interest rate in

US4 as our proxy of the global financial market.

4.

Methodology

The power-arch model was first introduced by Ding Granger and Engle in 1993. As

with the other ARCH family of models PARCH framework also allows the growth (y t)


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With

Where xit are explanatory variables including financial development, public deficit,

trade openness and US interest rate. (et) are independently and identically distributed

random variables with E(e_{t})=E(e_{t}²-1)=0, while (ht) is the conditional variance

of growth.

Further, follow the PARCH(1,1) process ht has alternative variance specification

as :

⑵

With

Where α and β are standard GARCH parameters, δ with δ ＞ 0 is the

heteroscedasticity parameter and γ is the level term for the lth lag of growth.

A feature of conventional ARCH model is that the conditional variance is related to

lagged absolute of squared residuals and lagged conditional standard deviation or


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growth and its volatility. We present our main reasons in three different aspects: the

direct, indirect and dynamic effect (short and long – term). The tables below report

the estimated parameters of interest for the period 1870 to 2003. All results were

obtained by quasi-maximum likelihood estimation as implemented in EVIEWS.

According to the likelihood ratio and the minimum value of the information criteria,

we choose the best fitting estimations.

First model is specified with φ=γ= 0 to study the direct effect of our set of

explanatory variables to the growth. In testing both direct effect and indirect effect,

the explanatory variables are allowed in mean equation and variance equation (model

1 and model 2). Through All three estimated parameters - γ, φ and λ , obtained from

the estimations, we can clearly investigate the implications of each variable on the

growth and its volatility. Then, instead of λ , we allow multiple explanatory variables

in the variance equation to specify the robustness of the indirect effect. Apart from

that, multiple variables allowed can also explaining how those variables works

together.

5. Empirical Results


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growth rate is positive and statistically significant, whereas, public deficits and

international financial development shows a mixed effect and are not significant at all.

A simple PARCH(1,1) model shows that International financial development seems to

have no effect on the growth (insignificant). However, whether these will be our

baseline results or changed from more profound estimations will be assessed below.

“K” is the garch in mean parameter. For all cases the estimates are positive and

highly significant. This is in a line with the theoretical argument of Black (1987).

Further, the power term coefficient “δ” are stable around 0.8 – 0.9.

It seems to be generally agreed that financial development is positively associated

with output growth. However, recently, several papers have pointed out that this

positive relationship between financial development and economic growth is actually

dependent on whether this movement is temporary or permanent. Loayaza and

Ranciere (2006), using a sample of 75 countries, studied the dual effect of financial

development on the growth, Campos, Karanasos and Tan (2008) adopt a similar

approach to examine the effects in Argentina. Both papers confirmed that a positive

long-term relationship between financial development and out put growth can coexist

with a negative short-term effect.


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Table 2 presents the estimated results. According to different financial development

measures, table 2.1, 2.2, and 2.3 report the estimates from money supply and

efficiency of the financial sectors respectively. “λ ” captures the short-run effect, “l” is

lag length and t-statistic value reported in the parentheses.

It can be seen that λ fd is negative and statistically significant through out all tests

which confirm that, in Brazil’s case, we find a significantly negative short-term effect

of financial development to growth. Further, not only financial development but also

trade openness and public deficits are also negative and significant related to the

growth. Also, the results suggest that in the short – term, the development of global

financial market has a positive impact on Brazil’s output growth.

To summarize, both financial development and trade openness supports economic

growth in Brazil in the long term. However, probably due to fast growth, Brazil also

experienced banking crisis which lead to an extremely fragile financial system. Hence,

our empirically findings suggest that the positive long-run relationship between

financial development and out put growth in Brazil, is associated with a negative short

– run relationship. In addition, as to the degree of the trade openness and public

deficits, compare to the trade openness, public deficits present only a little proportion


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Indirect Effect:

Table 3 reveals the estimation results for each one of the explanatory variable effect

on both growth and its volatility. Besides confirming the results from previous

estimations again, the parameter we most interested is φ which captures the effect on

growth via volatility channel.

The results show all financial development, the degree of trade openness on the

conditional volatility economic growth rates is negative and significant, whereas the

estimated coefficients of public deficits and international financial development are

positive and significant.

Moreover, direct effects of both revenue and expenditure become positive and

significant here, while the public deficit – the over all affect of revenue minus

expenditures continue to have no direct impact. Additionally, the indirect effects of

revenue and expenditures shows a completely different sign from their over all effect.

Public deficits affect growth volatility negatively while revenue and expenditure

appears to have positive impact on the growth volatility themselves.

Further, in order to gain a profound explanation of the results from above and to

investigate the robustness of our results. We run the model with all four explanatory


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To this point the findings from empirical results are quite different from Abreu and

Verner (1997) who claimed that increased public sector savings proved to have only

small impact on GDP per capita. It can be seen from both table 4 and 5, the indirect

effect of financial development remains statistically significant throughout with a

negative implication. Together with the results from estimation of the direct effects,

we find strong evidences that financial development is associated with out put growth.

Next, comparing table 4 and 5, revenue, expenditures and public deficits remains

the same signs (φ3). That is, the volatility of growth is negatively associated with the

public deficit while positively affected by revenue and expenditures respectively.

In addition, for the entire test, φ2 shows a positive and significant sign which

implies there is strong evidence that international financial market affected the

volatility of Brazil’s economy growth. Notice that trade openness changes positive

when we put all other variables in. However, estimate parameters become

insignificant throughout the table 4 and table 5.

In conclusion, we find robust evidence that financial development, public deficit

and international financial development affect growth indirectly – its volatility. Trade

openness has a negative and significant indirect effect itself, however, when we put all


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still has a strong impact on Brazil’s growth volatility.

Dynamic Aspects

In this section it will be discussed how short- and long-run effects help us to gain a

better understanding of our baseline results. In order to estimate the short- and

long-term relationships we follows the error correction (P)ARCH form:

Where θ and Ϛ are the short and long-term effect respectively, φ is the speed of

adjustment to the long-run relationship. The long-run growth equation is in the

parenthesis which acts as a forcing equilibrium condition:

Where εt is I (0). The lag of the first difference of each control variables characterizes

the short-run effect. Also we considering the PARCH effects by specified the error

term ut as following:

Where


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short-run effect, tells a totally different story. Almost all the control variables,

(including financial development, public deficits and trade openness) appear to have a

negative impact on growth in the short term.

More interestingly, results of US interest rate – a proxy of global financial

development, are contrary to the rest estimations. Links between output growth and

global financial development are negative in the long term and positive in the short

term.

Table 7 and 8 summarize the main results when we add 3 remaining explanatory

variables. Notice we only reported estimations from three variable combinations –

either financial development, public deficits and international financial development

or trade openness , public deficits and international financial development, because

the results of all four variable combinations is unstable with the PARCH process.

Basically, the results are in line with single variable test. φ lies within the dynamically

stable range (-2, 0). With the exception of the US interest rate, all other variables

appear to have a negative and significant impact on the short-run economic growth.

Also, the estimated parameters present a positive and statistically significant

association between financial development and growth in the long-term.


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in a long time period.

Thus, among other papers, our results are in a line with Loayaza and Ranciere

(2006) and Nauro, Menelaos and Bin (2008) who claim that the sign of the

relationship between economic growth and financial development depends on

whether the movements are temporary (short-run) or permanent (long-term).

Structural Breaks

In this section, we discuss one final important robust test regarding the role of

structural breaks. In order words, we assess whether taking account of structural

breaks will affect our baseline results. In testing the structural breaks in our various

explanatory variables, we adopted methodology developed by Bai and Perron (2003),

which can be download from: http://people.bu.edu/perron/code.html. The model

provided 3 different models including BIC, LWZ and B-P test model. Bai and Perron

address the problem of testing multiple structural breaks in “Computation and

Analysis of Multiple Structural Change models” (2003). In addition to test the

exsitsting breaks, B-P model also points out the number and location of multiple

breaks5.

http://people.bu.edu/perron/code.htmlhttp://people.bu.edu/perron/code.html


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effect in conditional variance equation as follows:

Generally speaking, our tested results are quite robust even with the inclusion of the

structural break dummies. It can be seen from tables 9 and 10, “λ ” is positive and

significant through out the estimations, and the coefficient of financial development

and trade openness are still positive and significant for the start of the sample until the

break year (λ > 0). Further, “λ + λ 1” capture our second half of the sample. The

estimations show that both financial development and Trade Openness promote

Brazil’s output growth whether structural break is included or not.

Except the direct effect, Table 10 also presented results from indirect effect. Overall,

results are quite robust, that is, financial development tends to affect GDP growth

negatively via the volatility channel (φ < 0 and φ + φ1 < 0). Nevertheless, indirect

effect of the trade openness becomes mixed – coefficients can be both positive and

negative, but they all insignificant within the dummy variables. Therefore, we can say

that the impact on trade openness to volatility of the growth lessened when we adopt

our structural break dummies. Next, as to long- and short-run effect, table 11 reveals


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negative short – run impact.. 2) Volatility of the output growth affected by financial

development negatively and interestingly, the indirect effect from trade openness to

growth volatility faded out when we add our structural break dummies.

6. Conclusions

The empirical evidence presented in this paper confirmed that financial

development, trade openness, public deficit and international financial development

are all endogenous variables in explaining the growth in Brazil’s economy, for a long

time period, from 1870 – 2003.

Although we included late nineteenth century and world war period, there is still

strong evidence that: firstly, over the long-run, financial development and trade

openness support and promote the output growth, however, the fast financial

development and out put growth may lead to financial fragility and banking crisis. In

Brazil’s case, by using an empirical model to test long-term and short-term in the

same time, we also find that a positive long – term relationship between financial

development and economic growth are also associate with a negative short – term

relationship.


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Empirical results also suggested that public deficit do affect growth via volatility

channel.

The finial observation is that though Brazil economy has had unstable links with

the world economy over the half of our examine period, we find clear clue that global

financial development links with Brazil’s economy – positively in the short run while

negatively in the short run.


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References

Abreu, M. and D.Verner, 1997. Long-Term Brazilian Economic Grwoth: 1930-1994.

Paris: OECD.

Adrogue, Ricardo, Martin Cerisola and Gaston Gelos, 2006. Brazil’s Long-Term

Grwoth Performance – Trying to Explain the Puzzle. IMF Working Paper

Angus Maddison, “Historical Statistics for the World Economy: 1-2003 AD”,at: http://www.ggdc.net/maddison/Historical_Statistics/horizontal-file_03-2007.xls

Beck, T., Levine, R. and N. Loayza, 2000. Finance and Sources of Growth. Journal

of Financial Economics 58, 261--300.

Beck,T., and R. Levine. 2004. Stock Markets, Banks and Growth: Panel Evidence.

Journal of Banking and Finance 28：423-442.

Campos, N, Armando Castellar Pinheiro, Fabio Giambiagi and Maurício M. Moreira,

2002. "Does it Take a Lula to go to Davos? A Brief Overview of Brazilian Reforms,

1980-2000," William Davidson Institute Working Papers Series 580, University of

Michigan.

Campos, N. and M. Karanasos, 2007. Financial Development, Economic Growth

and Political Instability Power-GARCH Evidence from Argentina 1896-2000,

Castelar Pinheiro, Armando, Indermit S. Gill, Luis Serven and Mark Roland

Thomas, 2004. Brazilian Economic Growth, 1900-2000: Lessons and Policy

Implications. Inter-American Development Bank.

Ding, Z., Granger, C.W.J. and R. Engle, 1993. A Long Memory Property of Stock

Market Returns and a New Model. Journal of Empirical Finance 1, 83-106.

https://owa1.brunel.ac.uk/exchweb/bin/redir.asp?URL=http://www.ggdc.net/maddison/Historical_Statistics/horizontal-file_03-2007.xlshttps://owa1.brunel.ac.uk/exchweb/bin/redir.asp?URL=http://www.ggdc.net/maddison/Historical_Statistics/horizontal-file_03-2007.xls


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Quarterly Journal of Economics 108, 717-737.

Levine, R., and S. Zervos. 1998. Stock Markets, Banks, and Economic Growth.

American Economic Review 88:537-558.

Loayza, N. V. and R. Rancière, 2006. Financial Development, Financial Fragility

and Growth. Journal of Money Credit and Banking, 38, 1051-1076.

Muinhos, Marcelo Kfoury and Marcio I. Nakane.2006.Comparing Equilibrium

Interest rates: Different Approaches to Measure Brazilian rates.Working Papers Series

from Central Bank of Brazil, Research Department. NO.101.

Manoel Bittencourt.2010. Financial Development and Economic Growth in Latin

America: Schumpeter is Right!. Working Paper Series. University of Pretoria.

Milton Friedman and Anna J. Schwartz. 1982. Monetary trends in the United States

and the United Kingdom : their relation to income, prices, and interest rates,

1867-1975, University of Chicago Press.

Mitchell. B. R. 2003. International Historical Statistics: The Americas: 1750 – 2000,

5th edition. PALGRAVE MAVMILLAN Press.

Patricia Stefani, 2007. Financial Development and Economic Growth in Brazil:

1986-2006. Economics Bulletin, Vol3, NO.69, 1-13

Peláez, C. M., and W. Suzigan. 1976. História Monetária do Brasil: Análise da

Política, Comportamento e Instituições Monetárias. Monografia (Instituto de

Planejamento Econômico e Social. Instituto de Pesquisas), no. 23. Rio de Janeiro,

TableA.3.

Ramey, G. and V. Ramey, 1995.Cross-country Evidence on the Link between

V l ili d G h A i E i R i 85 1138 1151


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Appendix

Table

Figure

GDP

0

200000

400000

600000

800000

1000000

1200000

1 8 7 0 9 8 7 6 5 4 3 2 1

1 9 6 0

1 9 6 9

1 9 7 8

1 9 8 7

1 9 9 6

GDP of Brazil from 1870 to 2003

1 8 7

1 8 8

1 8 9

1 9 0

1 9 1

1 9 2

1 9 3

1 9 4

1 9 5

GDP

Table 1. Direct effect on Economic Growth

x it ↓ k

Panel A: Financial DevelopmentM1

3.30

4. 314.32

0. 052.43

0. 442.58

0. 52

Deposits

(Commercial Banks) 3.714. 99

13 .59

0. 02332.42

0. 432.08

0. 48

Deposits

( Bank of Brazil ) 3.053. 27

4.91

0. 042.73

0. 522.83

0. 52

Panel B: Public Deficit

Expenditures3.06

3. 010.57

0. 00212.84

0. 543.29

0. 53

Revnues3.15

3. 350.55

0. 02132.76

0. 512.95

0. 53

Revenues - Expenditures3.84

5. 52−0.39

−0. 2382.45

0. 402.23

0. 48

Panel C: Trade Openness

−0. 80

−0. 80

−0. 90

−1. 00

−0. 90

−0. 80


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Table 3. Indirect effect on Economic Growth

x it ↓ k

Panel A: Financial Development

M12.15

6. 257.86

0. 03302.46

0. 411.49

0. 35−4.51

−0. 023

l−8

2.10

0. 12−

1. 00

M25.19

6. 43−0.30

−0. 00033.43

0. 461.73

0. 35−3.40

−0. 0036

l−5

4.16

0. 22−

1. 00

Deposits (Commercial Banks)1.61

2. 823.36

0. 05083.32

0. 502.46

0. 37−1.85

−0. 0131

l−5

1.35

0. 07−

1. 00

Deposits (Bank of Brazil)2.19

3. 223.54

0. 02773.59

0. 582.09

0. 30−3.24

−0. 0050

l−8

2.07

0. 06−

1. 2


Revenue2.80

4. 872.83

0. 01162.72

0. 432.21

0. 42−10.83

−0. 1014

l−5

1.30

0. 13−

0. 80

Expenditures2.53

4. 831.84

0. 000892.84

0. 432.12

0. 40−10.57

−0. 0094

l−5

1.31

0. 11−

0. 80

Revenue - Expenditures2.61

4. 74−0.92

−0. 00292.75

0. 451.97

0. 402.64

0. 0078

l−5

1.02

0. 08−

0. 80


Export2.74

6. 411.97

0. 04262.45

0. 391.69

0. 38−1.78

−0. 0288

l−8

2.08

0. 13−

1. 00

Import2.89

6. 693.77

0. 05385.65

0. 402.65

0. 32−1.85

−0. 0375

l−8

1.93

0. 11−

1. 00

Export Import2.28

6. 624.49

0. 021862.30

0. 381.73

0. 38−3.55

−0. 0199

l−

8

2.23

0. 14−

0. 80

Panel D: Interest Rate

US Interest Rate3.26

5. 072.36

0. 00133.06

0. 482.23

0. 421.66

0. 0010

l−8

3.68

0. 21−

0. 80

Table 3 reports parameter estimates for the following model:

yt c kh t x it t , ht 2 ht −1

2 ∣ et −1 ∣

ht −1

2 x it yt −6

The numbers in parentheses are absolute t statistics.

25


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Table 6. The short- and long-run Growth effects

xit ↓

Panel A: Financial Development

M1/GDP

l2

−1.10−0. 027

−7.44

−0. 483.45

0. 1332.65

0. 583.39

0. 52−

1. 00

Deposits/GDP

(Commercial Banks)l4

−4.83−0. 075

−6.72

−0. 464.05

0. 1672.78

0. 583.45

0. 51 1. 00

Deposits/GDP

( Bank of Brazil )l4

−4.86−0. 028

−6.91

−0. 474.08

0. 01652.85

0. 603.39

0. 51−

1. 00


Revenues

l7

−1.86−0. 0193

−8.91

−0. 65−1.60

−0. 03083.19

0. 534.03

0. 51−

0. 90

Expenditures

l7

−1.89−0. 0183

−9.09

−0. 66−1.69

−0. 03243.28

0. 494.06

0. 52−

0. 90

Revenue-Expenditure

l4

−1.62−0. 0056

−7.22

−0. 460.10

0. 000962.80

0. 503.37

0. 52−

0. 80


Export/GDPl5

−1.40−0. 056

−7.17−0. 55

2.847 0. 228

2.150. 48

3.290. 56

−1. 00

Import/GDP

l4

−9.06−0. 085

7.22−0. 48

3.400. 210

2.670. 57

3.380. 52

−1. 00

ExportImport

l4

−8.44−0. 047

−7.23−0. 48

3.26

0. 1162.61

0. 563.39

0. 53−

1. 00

Panel D: International Financial Development

US interest rate

l2

3.370. 00039

−9.82−0. 683

−2.50−0. 00028

2.620. 61

4.430. 60

−1. 00

Table 5 reports parameter estimates for t he following model:

Δ yt Δ xi,t −l yt −1 − c − xi,t −1 u t ,

h t 2 |ut −1 |

ht −1

2 . (l is the order of the lag)

and capture t he short- and long-run effects respectively.

indicates the speed of adjustment to the long-run relationship.

The numbers in p arentheses are absolute t statistics.

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Table 11. The short- and long-run Growth effects with dummies

xit ↓

M1/GDP

l4

−4.23−0. 045

−8.95

−0. 574.25

0. 1290.87

0. 0030

l−1

−2.00

−0. 1492.96

0. 513.70

0. 56−

1. 00

Deposits/GDP

(Commercial Banks)l4

−4.50−0. 065

−7.47−0. 50

4.190. 174

1.070. 0043

l−2

−2.54−0. 148

3.220. 57

3.240. 50 1. 00

Deposits/GDP

( Bank of Brazil )l4

−4.34−0. 028

−7.79

−0. 544.19

0. 0160.89

0. 0030

l−2

4.08

−0. 122.68

0. 503.77

0. 56−

1. 00

Panel B: Trade Openness

Export/GDP

l4

−3.75−0. 097

−7.41−0. 50

3.620. 277

0.77

0. 0026

l−2

−2.06

−0. 1152.920. 54

3.590. 53

−1. 00

Import/GDP

l4

−3.92−0. 069

−8.21−0. 53

4.020. 208

0.88

0. 0030

l−2

−2.77

−0. 1312.960. 54

3.550. 53

−1. 00

ExportImport

l4

−4.07−0415

−7.97−0. 52

3.870. 119

0.83

0. 0028

l−2

−2.48

−0. 1252.940. 54

3.580. 54

−1. 00

Panel C: Public Deficit

Export

l−4

−2.42−0. 0032

−8.48−0. 68

−0.86−0. 0017

0.800. 0026

l−2

−1.03−0. 109

2.620. 44

3.340. 56

−0. 90

Import

l−4

−2.76−0. 0042

−8.87−0. 64

0.280. 0053

0.550. 0016

l−2

−1.09−0. 092

2.400. 47

3.990. 60

−1. 00

ExportImport

l−4

−1.80−0. 0072

−7.03−0. 53

0.270. 0022

0.810. 0025

l−2

−0.73−0. 079

2.410. 51

3.650. 57

−1. 00

Panel D: US interest rate

US interest rate

l−4

2.060. 0009

−11.64−0. 75

−4.07−0. 0004

1.120. 0036

l−2

−1.88−0. 137

2.940. 55

3.810. 56

−1. 00

Table 5 reports parameter estimates for the following model:

Δ y t Δ x i,t −l y t −1 − c − xi,t −1 u t ,

h t 2 Dgdp |u t −1 |

h

t −1

2 yt −l . (l is the order of the lag)

and capture the short- and long-run effects respectively.

indicates the speed of adjustment to the long-run relationship.

The numbers in p arentheses are absolute t statistics.

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