Exchange Rate Volatility and its Effect on Macroeconomic Management in

COMESA Member States: The Case of Uganda

Jacob Opolot1

Research DepartmentBank of Uganda

AbstractThe paper empirically investigates the impact of exchange rate volatility on selected macroeconomic variables in Uganda. First, a Structural Vector Autoregressive model is used to evaluate the impact of exchange rate shocks on domestic prices, private sector credit, domestic interest rates, imports and exports. The results of impulse response functions suggest that innovations to the nominal exchange rate have profound implications for these macroeconomic variables. Second, the impact of exchange rate volatility on exports is examined using both Linear and Non-linear Autoregressive Distributed lag Models. The exchange rate volatility variable is computed using a generalized autoregressive conditional heteroskedesticity process. In order to implement the Nonlinear Autoregressive Distributed lag Model, the exchange rate series is decomposed into its positive and negative partial sum, where each partial sum captures the effect of either depreciation or appreciation. We also consider a two threshold case, since the behavior of exports may differ depending on the level of exchange rate changes. In order to ensure that there is a comparable number of observations in each case, 30% and 70% quantiles of exchange rate changes are used as thresholds to distinguish small from large exchange rate movements. The 20% and 80% quantiles are used to corroborate the results. In order to incorporate this into the model, the exchange rate series is split into three partial sums. The

1 This report is prepared for the COMESA Monetary Institute (CMI). The views expressed herein are those of the author and do not in any way represent the official position of the Bank of Uganda. This paper should not therefore be reported as representing the views of the Bank of Uganda or its management.

empirical results indicate that assuming linearity in export demand functions may be too restrictive given the superiority of the Non-linear Autoregressive Distributed lag Model over the Linear Autoregressive Distributed lag Models. The “one threshold model”, which distinguishes exchange rate changes between appreciations and depreciations, indicates a non-linear relationship, and that exports respond more to appreciations than to depreciations. The results of the “two threshold model”, which distinguishes large and moderate exchange rate changes, indicate that large exchange rate changes have a more profound effect on exports than small exchange rate changes.

1 Macroeconomic Performance1.1 Economic ActivityUganda’s macroeconomic performance has been remarkable since the early 1990s with real GDP growth averaging 7.0 per annum, although a relative slowdown was recorded in the aftermath of 2008/09 global financial crisis which led to global economic slowdown and a decline in external demand thereby curtailing growth in Ugandan exports, foreign direct investment and private transfer. Heightened financial volatility, risks of financial contagion and the ensuing global economic uncertainty led to a further drag on growth. Real GDP growth consequently decelerated to 3.4% in FY 2011/12. Real economic activity has however recovery, with real GDP growth averaging 5.9% during the last two years. Trends in Real GDP growth since FY 1999/00 are shown in Figure 1.1.

Figure 1.1: Uganda’s Economic Growth Trend: 2000 – 2014

Source: Uganda Bureau of Statistics

This recovery has in part been facilitated by prudent macroeconomic policy management that ensured inflation and exchange rate stability, thus providing an enabling macroeconomic environment for growth; strong foreign direct investment inflows; increased public investment in infrastructure; stronger growth in domestic demand on account of increase in real incomes and the recovery in private sector credit growth, which has facilitated private investment.

Medium-term forecasts indicate a consolidation of this trend, with real GDP growth projected at about 7% over the medium-term, supported by prudent macroeconomic management and the multiplier effects of public investment on infrastructure, and foreign direct investment especially to the oil, telecommunications, banking and the manufacturing sectors.

There is still some slack in the economy which will allow real GDP growth to accelerate without facing supply side constraints. Growth prospects, however, continue to be hampered by adverse external and domestic shocks.

1.2 InflationDuring the 1970s and 1980s Uganda experienced high and volatile inflation, with annual headline inflation peaking at about 200 in 1987. The high inflation rate during this time was attributed to the monetization of fiscal deficits and the structural weaknesses in the economy that led to acute shortages of commodities in the economy. The economic recovery program (ERP) introduced in late 1987 culminated in enhanced fiscal discipline and a remarkable reduction in inflation as the government consistently pursued fiscal discipline in its budgetary operations with the overall objective of containing fiscal deficits to levels that could be financed on a sustainable basis while at the same time reducing government indebtedness to the banking sector. Despite the huge weather-related shocks to prices, depicted by the trend of the Headline inflation, Bank of Uganda managed to keep core inflation (i.e. price changes that exclude food crop prices) very stable at around 5 percent per annum during most of the 1990s and early 2000s. Nonetheless, inflation remained remarkably variable as shown in Figure 1.2.

Figure1.2: Inflation Developments, 2000 – 2014

Source: Uganda Bureau of Statistics

Annual headline and core inflation which rose to 15.6 percent and 13.4 percent, respectively in August 2008, declined to single digit levels for most of 2010 before rising to peak at 30.4 percent and 30.8 percent, respectively in October 2011. The rise in inflation in 2008/09 was largely driven by rising international food and fuel prices as well as the effect of a prolonged drought on domestic food production which exerted significant

upward pressure on prices. In particular, average annual food crops inflation increased substantially to an average of 27 per cent in the first 5 months of 2008/09, up from 5.6 per cent registered in the second half of 2007/08. Sustained exchange rate depreciation also pushed-up prices of tradables, thus conpounding the already heightened inflationary pressures. The annual headline inflation rate dropped steadily from August 2008 through to March 2009, in part due to the softening of international commodity prices.

In FY 2011/12, supply side shocks to domestic food and international oil prices exerted significant upward pressure on domestic inflation, with headline inflation peaking at 30.5 per cent in October 2011. The increase in regional demand for Uganda’s food exports, mainly arising from neighboring Sudan and D.R.Congo also contributed significantly to food price increases during this period. In order to abate the rising inflationary pressures, Bank of Uganda implemented a tight monetary policy stance, increasing the reference rate, the Central Bank Rate (CBR) from 13 per cent in July 2011 to 23 per cent in November 2011. Consequently, the heightened inflationary pressures eased, with headline inflation declining to 5.8% in 2012/13.

1.3 Balance of PaymentsThe Balance of Payments is an important indicator of a country's external position. It measures the relative performance of the domestic economy against the global economy and provides a framework for identifying external economic imbalances and their implications for the domestic economy. Uganda’s current account balance continues to be weak, largely driven by higher import growth relative to export growth. Exports, which in part depend on global performance and outlook, constitute an injection since the boost domestic income through the multiplier process. Imports, on the other hand, are a withdrawal and reflect an outflow of demand and income.

Over the last ten years, the current account deficit as a percentage of GDP has averaged 6%. However, a significant deterioration was registetred in the aftermath of the global financial crisis and global economic downturn, when it deteriorated to about 10% of GDP. The import bill as a percent of GDP rose from 15 percent iin 2000 to about 25 percent in 2013, about double the export earnings to GDP ratio, which stood at 12.6 percent in 2013. The high import bill is however driven by high imports of intermediate and capital goods, which should in turn enhance the productive capacity of the economy.

The current account deficit has been largely funded by the surplusses in the capital and financial account of the balance of payments on account of strong foreign direct investments and portfolio inflows. The stock of reserves at the end of April 2014 amounted to US$3,401.57 million, equivalent to 4.5 months of future imports of goods and services. Developments in the main components of the balance of payments are shown in Figure 1.3.

Figure 1.3: Evolution of Uganda’s Balance of Payments and its Components: 2000 – 2014

Source: Bank of Uganda

2 Exchange Rate Policy and Developments in Uganda2.1 Exchange Rate PolicyDuring the 1960s and 1970s, Uganda pursued a fixed exchange rate regime. In the 1970s, a parallel foreign exchange market emerged due to the acute shortage of foreign exchange. The premium between the official exchange rate and the parallel market rate rose steadily, and by 1981, the exchange rate in the parallel market was more than 30 times the official exchange rate. In an effort to restore macroeconomic stability, a first reform programme was initiated in 1982, in which a managed float exchange rate regime was introduced. To cater for essential and non-essential trades, a dual exchange rate regime was adopted in 1983. In 1984, these two windows were merged before the country reverted to a fixed exchange rate regime in 1986. In May 1987, a currency reform was effected which saw a 77 percent devaluation of the exchange rate to try to correct for external sector imbalances (see Atingi-Ego and Sebudde (2007).

Effective July 1989, government adopted a crawling Peg, where the exchange rate was adjusted by the inflation differential between Uganda and her trading partners of Kenya, UK and US on a monthly basis. In 1990, a bold step was taken to legalise the parallel exchange market with the licensing of foreign exchange bureaux as money shops. The legalization of the parallel exchange market gave way to introduction of a weekly Dutch auction of donor funds at BoU, further reducing the foreign exchange premium. In November 1993, the Interbank Foreign Exchange Market (IFEM) was introduced, culminating into the complete liberalization of foreign exchange and payment systems in Uganda.

Currently, the IMF (2012) classifies Uganda’s exchange rate regime as a “floating exchange rate regime”. BOU’s involvement in the foreign exchange market is limited to occasional interventions to dampen excessive exchange rate volatility. The floating exchange rate system has, nonetheless, presented certain difficulties for the country. First, it has heightened the risk of exchange rate volatility, which is synonymous with the flexible exchange rate system. Second, the adoption of a flexible exchange rate system meant the loss of the exchange rate as a nominal anchor for domestic prices. Finally, the operation of an efficient foreign exchange market may not be technically feasible in a situation where financial markets are underdeveloped.2

2 In view of the linkages between different financial operations, some degree of development in other financial markets is necessary to support the operation of a smoothly functioning foreign exchange market with a floating rate.

2.2 Exchange Rate DevelopmentsExchange rate is an important price in an economy, which not only influences business decisions but also affects the competitiveness of the domestic traded goods sector. Exchange rates directly affect the realized return on an investment portfolio, denominated in more than one currency. Although desirable attribute of the floating exchange rate regime is the constant fluctuation against other currencies, which in part allows for efficient absorption of shocks to the balance of payments and structural adjustment of the economy to balance forces of external demand and supply, excessive volatility creates a lot of business uncertainty, which may be disruptive to investment and economic activity. Like other asset prices, exchange rate variation is caused both fundamental and non-fundamental factors. While exchange rate fundamentals explain the long-run behaviour of the exchange rate, their effectiveness in predicting future short-run changes in the exchange rates is limited by the propensity for the unexpected to occur. Consequently, the short-run behaviour of the exchange rate is greatly influenced by unpredictable economic shocks and news.

3 Exchange rate volatility and macroeconomic performance 3.1 Theoretical and Empirical Literature3.1.1Theoretical LiteratureThere is a plethora of theories that try to explain the impact of exchange rate volatilities on macroeconomic performance. From a theoretical point of view, the relationship between the exchange rate volatility and macroeconomic performance is at best ambiguous. Proponents of fixed exchange rate regime argue that nominal exchange rate instability reduces growth by impeding trade and increasing macroeconomic instability. The uncertainty associated with high exchange rate volatility increases risk to a typical risk-averse economic agent and consequently reduces the flow of trade and consequently growth. An increase in exchange rate uncertainty also reduces price transparency, which reduces the efficiency of the price mechanism in the allocation of resources, thereby impeding the efficiency of the financial intermediation process. Furthermore, if there are credit constraints, or if investment is irreversible, higher aggregate nominal exchange rate volatility is likely to translate into lower growth.

In contrast, proponents of flexible exchange rate regimes emphasize the advantage of exchange rate flexibility in correcting domestic and external

disequilibria in the face of real asymmetric shocks. They argue that when a country is hit by real asymmetric shocks, and prices and wages adjust slowly, flexible exchange rates can adjust relative international prices to compensate for output losses. It is also argued that trade volume would rise because the associated expected cash flow from exporting will grow faster than entry/exit costs when exchange rate volatility increases3 and that when financial markets are perfect, the expected profit margin might increase with increased exchange rate risk, if export prices are in foreign currency (see Mundell, 1961; McKinnon, 1973; De Grauwe, 1988; Giovannini, 1988; Franke, 1991; Rose, 2000; Fischer, 2001; Frankel and Rose, 2002, De Grauwe, 2005; Schnabl, 2007).

Exchange rate volatility may discourage credit growth by increasing risk premia and interest rates, which may consequently impede investment and consequently growth. It has also been argued that exchange rate stability increases the possibility that lending growth accelerates to a level no longer justified by fundamentals, thus creating an 'excessive’ credit development. The impact of exchange rate on FDI is also not unambiguous. From a theoretical point of view, one could expect that countries with less volatile exchange rates should be more attractive to foreign investors. At the same time, if foreign investors are not risk-averse and expect to obtain extra profit exchange rate uncertainty (Darby et al., 1999), exchange rate volatility may have positive impact on FDI.

3.1.2Empirical literatureThe impact real exchange rate volatility on macroeconomic performance in both developed and developing countries is well document in the empirical literature. Clark et al. (2004) found no evidence of a negative relationship between exchange rate volatility and trade and where some negative relationship has been found, the impact was minor. Azeez et al. (2012) found that exchange rate volatility has a positive effect on macroeconomic performance in Nigeria, both in the long and short run. They argue that during appreciation pressures, Nigerian firms take advantage and import required capital and technology, hence the positive effect of large exchange rate movements on macroeconomic performance.

Ogun et al. (2012) find a negative and lagged relationship between movements in the real exchange rate and FDI inflows in selected Sub-saharan African (SSA) countries. Ito and Sato (2006) examined the effects

3 Franke (1991) shows that a trading firm raises exports when exchange rate volatility reaches a certain threshold point such that the associated present value of cash flows is greater than that of the entry and exit costs.

of a pass-through of exchange rate changes on domestic prices in the East Asian economies using the VAR approach and their findings revealed high exchange rate pass-through to import prices but generally low pass-through to consumer prices. Danmola (2013) in his analysis of the impact of exchange rate volatility on the macroeconomic variables in Nigeria, finds that exchange rate volatility has a positive influence on economic growth, FDI and trade openness and a negative impact on inflation. Kihangire (2004) using disaggregated export data found that exchange rate variability has a negative impact on Uganda’s exports. He argued that policy interventions must not only address the competitiveness of the exchange rate but must also pay attention to exchange rate variability.

3.3 Methodology3.3.1Empirical FrameworkWe implement a VAR framework, a dynamic system of equations in which the current level of each variable depends on past movements of that variable and all other variables in the system to analyze macroeconomic aspects of the real exchange rate volatility in Uganda. As a first step, assume that the Ugandan economy is adequately represented by a structural model specified in equation (3.1).

.....................................................................................................................(3.1)

where is a matrix polynomial in the lag operator; is a matrix polynomial in the lag operator; is a vector of endogenous variables; is a vector of exogenous variables; is a vector of deterministic effects (time trend and seasonal dummies); and is a vector of structural disturbances, with , where is a diagonal matrix.The residuals of vector, , represent unexplained movements in variables (i.e. effects of exogenous shocks hitting the system); however as complex functions of structural shocks, they have no economic interpretation. Structural shocks are recovered using transformations of true-form representation into reduced-form and by imposing a number of identifying restrictions, which reflect some general assumptions about the underlying structure of the economy.

The reduced-form VAR corresponding to the structural model defined in equation (3.1) is specified as:

............................................................................................................(3.2)

Where and are matrices polynomials; is a vector of reduced-form disturbances, with ; and all other variables as previously defined.

If we define as a contemporaneous matrix in the structural form, and as a coefficient matrix in without contemporaneous coefficients,

then this relationship can be expressed as given in equation (3.3).

......................................................................................................................................(3.3)

The structural and reduced-form relationships can therefore be represented by equations (3.4) and (3.5), respectively.

.......................................................................................................................................(3.4)

....................................................................................................................................(3.5)

With the error terms defined as and . This relationship implies that:

...........................................................................................................................................(3.6)

Consistent estimates of and are deduced from estimates of , which can be obtained by the maximum likelihood estimation.

Since the right-hand side contains free parameters to be estimated, while the left-hand side contains only parameters, restrictions are needed for the system to be identified. Normalization of the diagonal elements of to unity leaves additional restrictions.

3.3.2 Identification Scheme An analysis of long-run, short-run, and contemporaneous relationships between endogenous variables in the system requires restrictions on the correlation structure of the residuals. In other words, identification focuses

on the errors of the system that are interpreted as linear combinations of exogenous shocks presenting the effect of a unit shock of one variable on another variable. Three approaches have been extensively used in the empirical literature. First, the Cholesky decomposition, together with the assumption that the contemporaneous relationships between variables have a recursive structure, which results in a temporal ordering of the variables. The second approach uses the information given by the history of the variables through impulse response functions. The third is the Structural Vector Autoregressive (SVAR) approach, in which restrictions to identify the structural components of the error terms are derived from economic theory.

We use a Structural Vector Autoregressive (SVAR) approach, which allows for contemporaneous feedback between variables, while imposing the minimal structural restrictions on the model (see Sims and Zha; 1998 and Kim and Roubini; 2000). In this specification, the consumer price index, private sector credit, 364-day treasury bill rate, import volumes, export volumes and the nominal exchange rate are entered as endogenous variables. We assume that the consumer price index is affected by innovations to private sector credit and the exchange rate; broad money is affected by innovations to prices, interest rates and the exchange rate; interest rates are affected by innovations to private sector credit and exchange rate; import demand is affected by innovations to the exchange rate; exports are affected by innovations to domestic prices and the exchange rate; and the exchange rate is affected by innovations to domestic prices, money supply, interest rates, and exports. These restrictions are depicted in equation (3.7).

......................................................

......(3.7)

We also include exogenous variables to control for broader global economic developments, which have implications for small open economies, like Uganda. For this purpose, we include the GDP of the United States, which we use as a proxy for the performance of the world

economy. We also control for global demand and inflation developments by including foreign consumer prices and world oil prices as exogenous variables. We add also a dummy to control for the 2008/09 financial crisis. The causal impacts are summarized using impulse response functions.

3.3.3Data and Sample characteristicsThe VAR model is estimated using quarterly data for the period 1995:01 to 2014:06. All variables are expressed in logarithms, except the treasury bills rate which is expressed as a percentage of GDP. Centred seasonal dummies are used in the estimations instead of pre-adjusting the series for seasonality. The first step in the analysis is to establish the time series properties of the data. The evolution of the variables is shown in Appendix 1. All variables appear to contain a deterministic trend. The time series properties are further investigated using the augmented Dickey-Fuller [ADF] (1979) and Phillips-Peron [PP] (1988) unit-roots tests. The null hypothesis under both tests is that the time series are generated by unit root processes.4 As shown in Table 3.1, all variables appear to have a unit root and become stationary after first differencing, that is, are integrated of order one, I(1).

Table 3.1: Time series properties of the dataLevels First Difference

ADF PP Inference ADF PP InferenceTB rate -4.5468 -3.8731 Stationary - - -lcore

-0.9387-0.8854 Non-

stationary-7.4669 -11.6690 Stationary

lexport -0.1188 -0.2482 Non-stationary

-10.9394 -21.5358 Stationary

limport -0.3146 -0.5218 Non-stationary

-15.7722 -18.7682 Stationary

lpsc -0.2659 -0.2447 Non-stationary

-16.3156 -16.2871 Stationary

lexrt -1.6883 -1.7131 Non-stationary

-11.1435 -10.9436 Stationary

The superscripts ** and * denote rejection of the hypothesis of a unit root at 1% and 5% significance levels respectively. The critical statistics are -3.4582 and -2.8737 at 1% and 5% respectively. The respective probabilities are indicated in the parenthesis.Source: Authors computations

4 The null of a unit root against a one-sided alternative is rejected if the test statistic is less than the critical value.

Having established the time series properties of the variables, we proceed to test for cointegration. Since the objective of VAR analysis in this study is to assess the interrelationships between the variables rather the parameter estimates, we proceed and estimate the VAR in levels. Estimating the VAR in levels allows explicit cointegrating relationships and preserves maximum information in the data (see Sims; 1980), Sims et al.; 1990, and Enders; 2004). McCallum (1993) also argues that the estimation of SVAR in levels is appropriate if the error terms of each VAR equations are stationary and serially uncorrelated. Standard information criteria are used to select the lag lengths of the estimated SVARs. The SVAR model is estimated using three lags.

3.4 Empirical Results3.4.1CointegrationSince most of the variables are non-stationary in levels, we test the time series for co-integration using Johansen and Juselius (1990) and Johansen (1991) maximum likelihood framework. The basic premise underlying cointegration is that if, in the long-run, two or more series move closely together, even though the individual series are trended, the difference between them is constant. It is therefore possible to consider these series as defining a long-run equilibrium relationship, since the difference between them is stationary (see Hall and Henry; 1989). Absence of co-integration however, implies that such variables have no long-run relationship and in principal can drift arbitrarily far away from each other (Dickey et. al.; 1991).

We found it reasonable to include the interest rate variables (which is I(0)) in the cointegrating vector. The appropriate lag structure of three was determined using the Akaike Information Criterion (AIC) and the Schwarz Bayesian Criterion (SBC). The results of cointegration analysis5 are presented in table 3.2. Table 3.2: Multivariate Co-integration Results Eigen value 0.23 0.16 0.08 0.07 0.04 0.004Null hypothesis1 r=0 r 1 r 2 r 3 r 4 r 5

147.01 86.13 46.90 26.82 8.88 0.01

60.88 39.23 20.08 17.94 8.87 0.01

95% critical value (trace)95.75

(0.0005)69.82

(0.002*)47.86

(0.061)29.80

(0.106)15.50

(0.376)3.84

(0.919)5 The detailed results are not reported here for brevity. However, like any other empirical results, they are available upon request from the author.

*

95% critical value (Max)

40.08 (0.0001)

*33.88

(0.01**)27.58

(0.3355)21.13

(0.1322)14.26

(0.2970)3.84

(0.919)1. r is the number of cointegrating vectors.2. The statistics and are respectively the Johansen maximal and trace

eigenvalue statistics for testing cointegration. The null hypothesis is in terms of r (the number of cointegrating vectors), and rejection of r=0 is evidence in favour of at least one cointegrating vector.

3. The critical values for both the maximal and trace statistics are taken from Osterwald-Lenum (1992).

4. ** and * denote 1% and 5% significance levels, respectively. 5. The probability values are presented in the parenthesis.Source: Authors Computations

As shown in table 3.2, both the maximal eigen statistic and the trace statistic indicate the existence of two cointegrating vectors. The null hypothesis r = 0 and r ≤ 1 can clearly be rejected in favour of two cointegrating vectors.

3.4.2Structural VARSince the endogenous variables are cointegrated, the SVAR is estimated in levels. Estimating the SVAR in levels allows explicit cointegrating relationships and preserves maximum information in the data. An important aspect in the specification of the SVAR is the determination of the lag order of the autoregressive lag polynomial since all inference in the VAR model depends on the correct model specification. We use standard information criteria to determine the appropriate lag length that that ensures no serial correlation from the residuals of the estimated VAR. The diagnostic tests of the residuals also suggest that the residuals of the estimated VAR are well-behaved.

Robustness checks are also conducted to establish the stability of the estimated VAR. Indeed, model is stable, since the inverse of estimated coefficient matrix characteristic root is less than 1 and the inverse roots of the AR characteristic polynomial lie within the unit circle. The diagnostic tests of the residuals also suggest that the residuals of the estimated VARs are well-behaved (see Appendix 2).

In line with the objective of the paper, we focus on the impact of exchange rate shock on consumer prices, money supply, interest rates, exports and imports. The impulse response functions, which show the effect of a one standard deviation positive shock in the exchange rate to other

endogenous variables in the model are shown in Figure 3.1. The dotted lines represent a 95% confidence interval.

Figure 3.1: Impulse response functions

From the Figure, it seems clear that a positive shock to the exchange rate (defined as units of the Uganda shilling per US dollar) is followed by an increase in consumer prices. This is consistent with the empirical experience that exchange rate depreciation causes an increase in prices of tradable goods, which in turn leads to an increase in the general price level. The maximum impact of the shock on consumer prices is felt in the third month and there after dissipates but at a slower pace as the impact seems to be persistent. The impact on private sector credit is quite similar. A positive exchange rate shock is followed by an increase in money supply and private sector credit.

A positive exchange rate shock leads to an increase in Treasury bill rates; the maximum impact is felt within three months, but the impact seems to be neutral in the long-run as the impact dissipates within 15 months. Theoretically, the impact of exchange rate depreciation on the current account balance of the balance of payments depends on the Marshall-Lerner condition and the elasticity of demand for exports and imports. If demand for imports and exports is elastic, then a depreciation will improve

the current account balance, by increasing exports and reducing imports. As shown in figure 3.1, a positive shock to the exchange rate leads to decrease in imports, with a maximum impact being realized after 5 months. Indeed, demand for imports seems to be price elastic, with a positive shock (depreciation) leading to an increase in the price of imports which consequently leads to a decline in demand for imports. On the contrary, a positive exchange rate shock leads to a decline in exports, although the impact seems to be neutral in the long-run.

3.5 Concluding RemarksThe paper empirically investigates the impact of exchange rate shocks on selected macroeconomic variables in Uganda using monthly time series data for the period 1995:01 to 2014:06. The study was undertaken using a Structural Vector Autoregressive framework. The results of impulse response functions suggest that innovations to the nominal exchange rate have profound implications for the macroeconomy. A positive exchange rate shock (depreciation) increases the domestic price level, private sector credit, and domestic interest rates, but reduces imports and exports. The findings suggest that policy makers in Uganda should take into account the implications of exchange volatility in the design of appropriate macroeconomic stabilization policies.

4 Thresholds effect of exchange rate volatility on Exports4.1 Specification of Empirical Model4.1.1Baseline ModelConsider a conventional export demand function as specified in equation (4.1).

...............................................................................................................(4.1)

where is the volume of exports, is a measure of foreign income, is a measure of the relative price of exports, is a measures of

nominal exchange-rate volatility, and the subscript denotes time. We use nominal exchange rate volatility as opposed to real exchange rate volatility on the assumption that it is the nominal exchange rate variation and consequently risk that directly enters the decison-making framework of economic agents. The real exchange rate risk depends in effect not only on the variance of the nominal exchange rate, but also on that of relative prices, which constitute a different type of risk for economic agents. A volatility measure that is partly driven by fluctuations in relative price therefore does not distinguish between the risk associated with nominal exchange rate changes independent of price movements and the risk associated with all other factors which may affect domestic and foreign prices.

The effect of foreign income on exports is expected to be positive on the assumption that exports are normal goods. From the law of demand, the effect of price is expected to be negative, while the effect of exchange-rate volatility is ambiguous. Expressing equation (4.1) in log-linear form yields:

......................................................(4.2)

A priori expectation is that: is ambiguous.

4.1.2Threshold ModelA rational economic agent should expect the exchange rate to vary to some degree. Consequently, only when exchange rate volatility surpasses

a threshold value does it trigger agents’ reaction.6 Such a threshold effect of exchange rate volatility needs to be incorporated into the model specification to capture the inherent nonlinearity of the effect of exchange rate volatility. A threshold model is thus used to model the differential impact of exchange rate volatility on exports. Given the export demand function specfied in equation (4.2), we specificy a threshold model to capture the differential impact of exchange rate volatility on exports as given in equation (4.3).

for ............................(4.3a)

for ...............................(4.3b)

Where is the threshold value that activates the effect of exchange rate volatility on exports. The threshold point effectively splits the sample into two regimes “high volatility regime” and “low volatility regimes”.

The specification in equations (4.3a) and (4.3b) shows that the intercept term and the slope coefficient for the natural logarithm of exchange rate volatility defined in equation (4.2) could vary depending on whether the natural logarithm of exchange rate volatility is greater than or less than . To estimate the model, the threshold value and the value of slope parameter are estimated simultaneously.

Following Hansen (2000) and Yanhong Zhang et al. (2006) among others, an indicator variable, , is defined to capture the threshold effect. The inclusion of the indicator variable permits the specification of equations (4.3a) and (4.3b) as a single equation as defined in equation (4.4).

.............(4.4)

Where: , and .

From the specification in equation (4.4), if the threshold effect does exist, then should differ significantly.

6 Exchange rate risk in a flexible exchange rate regime should be defined as unexpected changes in the exchange rate.

4.2 Data, variables and sample characteristics4.2.1Data and VariablesThe export demand function is estimated using quarterly data for the period 1993:Q1 to 2013:Q4. The variables used in the estimation include export volumes, a measure of world income, relative price of Ugandan exports, and a measure of exchange rate volatility. Following a common practice in the empirical literature, export volumes are obtained by deflating export values by the export price index, which is by export unit values. The foreign economic activity variable is computed as a weighted average of Gross Domestic Products of Uganda’s major trading partners, while the relative price of exports is proxied by the ratio of the domestic consumer price index to the weighted average of the consumer price indices of Uganda’s major trading partners.

4.2.2Measure of exchange rate volatilityExchange rate volatility, which captures the risks faced by exporters due to unexpected fluctuations in the nominal exchange rates, is not directly observable. Thus, the first challenge in the empirical investigation is choosing an appropriate measure of exchange rate volatility. Most of the traditional literature has tended to use standard deviation of the moving average of the logarithm of the exchange. The standard deviation method has two distinct shortcomings. Firstly, it wrongly assumes that the empirical distribution of the exchange rate is normal and secondly, it discards the distinction between predictable and unpredictable elements in the exchange rate (Bah & Amusa, 2003; Aziakpono, et al., 2005). Indeed, it has capacity to capture potential effects of high and low peak values of the exchange rate and yet these peak values may contain the unpredictable factors which affects exports.

In order to circumvent the above shortcomings, we use a generalized autoregressive conditional heteroskedesticity (GARCH) process to model exchange rate volatility.7 This framework is appealing because it captures both volatility clustering and unconditional return distribution with heavy tails. The idea behind the ARCH framework is that the conditional variance of a series is not constant over time and volatility is clustered, which means that high volatility is most likely followed by high volatility.8 Hence, the conditional variance is not homoscedastic, but heteroscedastic. We 7 Seabra (1995) finds that GARCH outperforms other available measures of volatility. 8 Mandelbrot (1963) noted that in financial time series, large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes.

assume here that the process of the nominal exchange rate follows a random walk with drift and time-varying variance. In this model, the conditional variance depends not only on lagged disturbances, but also on its own lagged values.

Following Bollerslev (1986), we specify a GARCH (p, q) model with a constant as given in equation (4.5):

......................................................

..(4.5a)

.................................................................................

......................(4.5b)

Equation (4.5a) is the conditional mean equation, with an autoregressive process of order k, , while equation (4.5b) is the conditional variance equation specified as a GARCH model, where is the number of ARCH terms, and is the number of GARCH terms. is the exchange rate, is a difference operator, is the conditional variance of the error term ( ),is the constant of the conditional mean equation, is the constant of the

conditional variance equation, is the ARCH term and is the

GARCH term.

In the estimation, a GARCH (1, 1) was found to be parsimonious. Consequently, exchange rate volatility was estimated using a AR (1) - GARCH (1, 1) model. The results of the AR (1) - GARCH (1, 1) model presented in Table .1 suggest that the exchange rate follows a GARCH (1, 1) process.

Table 4.1: GARCH (1, 1) Model Results and Diagnostic TestsMean Equation

VariableCoefficient 0.093**

(0.010)0.287*

(0.097)Variance Equation

VariableCoefficient 0.522*

(0.204)0.414**(0.1161)

0.574**(0.075)

Log likelihoodDurbin-Watson Stat.KurtosisSkewnessARCHPORTM

213.52.087

2.37890.6199

F (2, 66) =0.6502 [0.420]Chi-Square (12) = 12.23

[0.413]Note: 1. The numbers in parentheses are standard errors.

2. The superscripts * and ** denote significant of coefficients at the 1% and 5%levels, respectively.

3. ARCH is the Engle (1982) test for until second order autoregressive conditional heteroscedasticity in the residuals and PORTM is the Portmanteau test for serial correlation.

Source: Authors Computations

The diagnostic tests indicate that the residuals are well behaved. Absence of serial correlation in the residuals implies no need to fit a higher order GARCH model to the data. Moreover, results from likelihood ratio tests suggest that the composite hypothesis p = q =1 is not rejected. Figure 5.1 shows the derived conditional exchange rate volatility generated from the estimation of equation (4.5).

Figure 4.1: Conditional Volatility of the Exchange Rate

-12

-8

-4

0

4

8

12

1998 2000 2002 2004 2006 2008 2010 2012 2014

4.2.3Unit root test

Since macroeconomic time-series data are usually non-stationary (Nelson and Plosser, 1982) and thus prone to spurious regression, unit root tests

are conducted to test whether the individual time series contain a unit root. For this purpose, the augmented Dickey-Fuller [ADF] (1979) and Phillips-Peron [PP] (1988) unit-roots tests are conducted.9

As shown in Table 4.2, all variables appear to have at least one unit root and become stationary after first differencing, save for the exchange rate volatility index, which is I(0). The stationarity of the exchange rate volatility index is consistent with the findings of most empirical work on exchange rate volatility (see, for example the discussion in Grier and Smallwood (2007) and Baum and Caglayan (2010), among others).

Table 4.2: Time series properties of the data

Variable

Level First differenceADF PP Inference ADF PP Inference

EXP-

2.321(0.4118)

-2.299(0.422

7)Non-

stationary-

6.5551(0.000)

-6.524(0.000

)Stationary

GDPf-

2.898(0.176)

-2.823(0.199

)Non-

stationary-

6.629(0.000)

-9.530(0.000

)Stationary

RP-

2.559(0.3000)

-1.870(0.648

)Non-

stationary-

4.794(0.003)

-4.732(0.003

)Stationary

EV-

6.618(0.000)

-6.488(0.000

)Stationary  

The numbers in parenthesis are the respective probabilities.

Source: Authors Computations

4.3 Estimation Framework Empirical Results4.3.1Baseline Model4.3.1.1 Empirical FrameworkSince exchange rate volatility is I(0) and the other variables are I(1), the Autoregressive Distributed lag (ARDL) model, the bounds testing approach to cointegration originally introduced by Pesaran and Shin (1999) and

9 The null hypothesis under both tests is that the time series are generated by unit root processes and the null of a unit root against a one-sided alternative is rejected if the test statistic is less than the critical value.

further extended by Pesaran et al. (2001) is employed.10 The ARDL representation of equation (4.2) is given in specified in equation (4.6).

…..(4.6)

Where is the difference operator, and all other variables as earlier defined. The first step in the ARDL bounds testing approach is to estimate Equation (5.6) by Ordinary Least Squares (OLS) and test for the existence of a long run (cointegration) relationship among the variables by conducting an F-test for the joint significance of the coefficients of the lagged levels of the variables, that is: the null hypothesis of no cointegration relationship, defined as: is tested against the alternative hypothesis of the existence of cointegrating relationship defined as: .

Pesaran and Pesaran (1997) and Pesaran et al. (2001) provide two sets of critical values for the cointegration test.11 If the computed F-statistic is greater than the upper critical bound, then the null hypothesis of no cointegration will be rejected. Conversely, if the computed F-statistic falls below the lower critical bounds value, then the null hypothesis of no cointegration is accepted. The test is however inconclusive if the computed F-statistic lies between the lower and upper bounds. In this case, unit root tests should be conducted to determine the order of integration of the variables. If all the variables are found to be I(0), then the decision is taken on the basis of the lower critical bound value. On the other hand, If all the variables are found to be I(1), then the decision is taken on the basis of the upper critical value.

Once a cointegrating relationship is established, the long run and error correction estimates of the ARDL model can be obtained from Equation 10 This approach, which is based on the estimation of an Unrestricted Error Correction Model (UECM) has several advantages over the conventional types of cointegration techniques. (i) It can be applied to a small sample size study (Pesaran et al., 2001). (ii) It estimates the short- and long-run components of the model simultaneously, removing problems associated with omitted variables and autocorrelation. (iii) The standard Wald or F-statistic used in the bounds test has a non-standard distribution under the null hypothesis of no-cointegration relationship between the examined variables, irrespective whether the underlying variables are I(0), I(1) or fractionally integrated. (iv)The endogeneity problems and inability to test hypotheses on the estimated coefficients in the long run associated with the Engle-Granger method are circumvented.11 The lower critical bound assumes that all the variables are I(0), meaning that there is no cointegration among the variables, while the upper bound assumes that all the variables are I(1).

(4.6). The parameter stability test for the appropriately selected ARDL representation of the error correction model can also be performed at this stage. A general error correction representation of Equation (4.6) is specified as:

…………………………………

(4.7)

Where the ECM term is a residual obtained from the estimated cointegration model; are the short-run dynamic coefficients of the model’s convergence to equilibrium; and is the adjustment parameter that measures the speed of adjustment towards the long-run equilibrium.

4.3.1.2 Empirical Results(a) Cointegration ResultsThe results of the ARDL model are presented in Table 4.3. The results of the bounds F-test for cointegration relationship based on Equation (5.6) are presented in panel A. The optimal lag length was selected on the basis of Akaike Information Criterion. The computed F-statistic is 3.4197, which is greater than the upper critical bound at the 10 percent significance level. Thus, the null hypothesis of no cointegration is rejected in favour of the alternative hypothesis of a stable long-run relationship at the 10% level of significance. This implies that the considered variables are cointegrated i.e. these series cannot move too far away from each other or they cannot move independently of each other. In addition, cointegration implies that there is some adjustment process in the short run, preventing the errors in the long run relationship from becoming explosive.

The existence of a cointegrating relationship between exports and a vector of explanatory variables defined in equation (4.1) allows us to proceed with the estimation of the long-run parameters of the model. Estimates of the long-run coefficients of the export demand function and the diagnostic test statistics of the estimated model are presented in Panel B. The overall fit with the adjusted of 0.27, is relatively low. The relative price and foreign economic activity variable have the expected theoretical signs and are significantly different from zero. The elasticity coefficient of foreign income is positive, which implies that a higher income level of foreign income partners leads to higher purchasing power thereby encourages more exports from Uganda. The estimated relative price elasticity is

negative and significant, but is close to unity (-1.062), which means that other things being constant, changes in the relative export price, will lead to a very small change in export earnings. The impact of exchange rate volatility is negative, but insignificant as shown in table 4.3.

Table 4.3: Long-run estimates of the Export Demand Function. Panel A: Bounds test

Computed F-Statistic 3.4197critical values

Lower bound Upper bound

1% significance level 5% significance level 10% significance level

3.41 4.68 2.62 3.79 2.26 3.35

Panel B: Long run estimatesDependent Variable (ln EXP)Regressor Coefficient p-valuesConstant -5.459 0.002

-1.062 0.0012.084 0.001-0.725 0.102

0.27Panel C: Short-run

Diagnostic Test statisticsM Version F version

A: Normality J-B = 1.180 [0.554] Not applicableB: Serial correlation Chi-Sq = 5.082 [0.240] F(1,10) = 2.125 [0.176]C: Heteroscedasticity Chi-Sq = 11.904

[0.806]F(1,11) = 0.451 [0.932]

D: Functional Form Chi-Sq = 0.198[0.623] F(1,25) = 0.256 [0.441]E: Stability: Recursive Estimates

CUSUM [Stable] CUSUM of Squares[Stable]

Source: Authors Computations

(b) Short-run DynamicsFollowing Engel and Granger (1987), an error correction model (ECM) is estimated to examine the short-run dynamic relationship of the export demand function. Indeed, Aziakpono, et al. (2005) argues that such a relationship represents an adjustment process by which the deviated actual export is anticipated to adjust back to its long-run equilibrium path, and thus reflecting the dynamics that exists between real exports and its

major determinants. The results of the error correction model of the ARDL approach, defined by equation (4.7) are presented in Table 4.4.

Table 4.4: Estimation results of the error correction model of the ARDL framework

Dependent Variable (LY)

Regressor Coefficient t-values p-valuesConstant -5.458550 -1.557782 0.1476ΔlnEXPV t-1 2.499861 5.308575 0.0002ΔlnEXPV t-3 0.824489 4.202381 0.0015ΔlnEXPV t-4 0.508248 2.833914 0.0163ΔlnEXPV t-5 0.297383 2.316010 0.0409ΔlnGDPf t-1 -2.338423 -5.399764 0.0002ΔlnGDPf t-2 -2.485850 -4.384538 0.0011ΔlnGDPf t-3 -1.332912 -2.660797 0.0222ΔlnGDPf t-5 1.206519 2.917619 0.0140ΔlnRPEX t-1 -0.666069 -3.327752 0.0067ΔlnRPEX t-1 1.205750 2.489228 0.0301ΔlnRPEX t-3 1.257722 2.950671 0.0132

EV t-2 0.157545 2.827411 0.0164ECMt-1 -0.0284301 -5.856192 0.0001

R-squared 0.603971    Mean dependent var 0.011813

Adjusted R-squared 0.755563    S.D. dependent var 0.095061

S.E. of regression 0.046999    Akaike info criterion -3.005422

Sum squared resid 0.024297    Schwarz criterion -2.156755

Log likelihood 61.57861    Hannan-Quinn criter. -2.739630

F-statistic 6.09112 [0.0021]     Durbin-Watson stat 2.395311

Source: Authors Computations

The coefficient of the lagged error correction term (-0.02843), is negative and statistically significant at the 1 percent level. This signifies the existence of a long-run equilibrium relationship between the variables. Nonetheless, the feedback coefficient is relatively low, suggesting a relatively weak adjustment back to equilibrium. Approximately 2 percent of the disequilibrium caused shocks during the previous quarter converges back the long-run equilibrium within the first quarter.

4.3.2Non-linearity in Export Demand4.3.2.1 Empirical FrameworkThe study employs the nonlinear ARDL framework developed by Shin et al. (2011) to investigate nonlinearities in the export demand function in

Uganda. This framework, which is a generalisation of the ARDL bounds testing approach of Pesaran et al. (2001) allows for the estimation of asymmetric long-run and short-run dynamic coefficients in a cointegration framework. The starting point is the export demand function specified in equation (4.2). To account for nonlinearities in export demand functions; we apply the Non-linear ARDL (NARDL) framework of Shin, Yu, and Greenwood-Nimmo (2011). In order to implement this framework, the original exchange rate series is first decomposed into its positive and negative partial sum, where each partial sum captures the effect of either depreciation or appreciation.

……………………………………………………,….…………………………………………………………………(4.8)

Which are defined as:

…………………………………………………………

…………………….…(4.8a)

…………………………………………………………

……………….……..(4.8b)The threshold for changes in the exchange rate is set to zero, since the mean of changes in the exchange rate during the period of study is virtually zoro. Since in this decomposition, we have both positive and negative values, the exchange rate variable will not be transformed into logs. The coefficients of the exchange rate variable cannot therefore be interpreted as elasticities. Nonetheless, nonlinearities with respect to the long-run can be determined and a one unit change in the exchange rate series approximately equals a one percentage change since changes in the exchange rate wander around unity. The magnitude of the exchange rate coefficient is therefore comparable to conventional elasticities.

Second, we also consider a two threshold case, since the behavior of exports may differ depending on the level of exchange rate changes. In order to ensure that there is a comparable number of observations in each case, 30% and 70% quantiles of exchange rate changes are used as thresholds to distinguish small from large exchange rate movements. The 20% and 80% quantiles are used to corroborate the results. This allows us to determine the degree of exchange rate changes which affects exports. In order to incorporate this into the model, the exchange rate series is split into three partial sums as shown in equation (4.9).

…………………………………………………………………………………………………………..(4.9)Which are defined as:

……………….

…………………………………………………….(4.9a)

………………………………………………

………….(4.9b)

……………………………………………………

……………….(4.9c)

Where is an indicator function defined as:

……………………………………………………………………….…….(4.9d)The decompositions for the one and two threshold cases are then incorporated into the export demand function defined in equation (4.2) to yield the respective threshold cases defined in equation (4.10).

One threshold case

…………………………………..(4.10a)

Two threshold case

………………(4.10b)

Following Shin et al.(2011), the nonlinear ARDL representations of equations (4.10a) and (4.10b) are in specified in equations (4.11a) and (4.11b), respectively.The coefficient of foreign demand is expected to be positive since an increased in foreign demand should stimulate exports, while the coefficient of relative prices (defined as domestic consumer price index divided by the foreign price index) is expected to be negative. All the coefficients of the exchange rate (defined as units of Uganda shilling per United States dollar) are expected to be positive since depreciation should stimulate exports while an appreciation should constrain exports.

If the exchange rate coefficients in the “one threshold case” differ significantly from each other, then this could be an indication of strategic pricing. On the other hand, in the “two threshold case” if is smaller than and , the exports react strongly to large exchange rate changes than to small changes.

One threshold case

…………….

………….....(4.11a)

Two threshold case

……….

(4.11b)

In the “one threshold case”, the NARDL nests the ARDL model presented by Pesaran et al. (2001) when and . In the “two threshold case”, the same holds when and for all . Verheyen (2010), also argues that if nonlinearity is not rejected, then the NARDL models should nest the ARDL and the coefficients should be of the same magnitude. The NARDL is therefore less restrictive than the linear model.

Testing for a cointegrating relationship amounts to testing the null hypothesis in the one threshold case and the null hypothesis in the two threshold case. Since the distribution of this test is non-standard, Pesaran et al. (2001) provides critical values for the cointegration test. The restrictions on short-or long-run symmetry can however be tested by conventional Wald tests. The

appropriate lag length of the first differences in the NARDL is chosen using conventional criteria. 4.3.2.2 Empirical ResultsThe results of the “one threshold case” are presented in Table 4.5. The evidence in favour of cointegration is very strong. The computed is 4.7491, which is greater than the upper critical bound at the 1 percent significance level. Thus, the null hypothesis of no cointegration is rejected in favour of the alternative hypothesis of a stable long-run relationship. The overall fit is reasonable, with adjusted of 0.412. The adjusted for the “one threshold case” of the NARDL is larger than the ARDL, indicating superiority of the NARDL model over the ARDL. A formal test of long-run symmetry is also conducted, and as can be seen from Table 4.5, the hypothesis of long-run symmetry (both exchange rate coefficients are equal) is rejected, implying the existence of a non-linear relationship. The exchange rate coefficients have got the expected positive signs (which cannot be interpreted as elasticities) as an increase in the exchange rate corresponds to a depreciation of the Uganda shilling against the United States dollar. The positive sign implies a positive relationship between exports and the exchange rate. The exchange rate coefficients indicate that exports react strongly to appreciations than to depreciations, that is, exports benefit less from depreciations than they suffer from appreciations. The relative price and a proxy for foreign income have the expected respective negative and positive coefficients, which are also significant.

Table 4.5: Results of the Non-linear Autoregressive Model, One threshold caseDep. Variable: Regressor Coefficient t-values p-values

2.458 2.757 0.047-0.499 -5.308 0.002

0.324 4.202 0.0050.508 2.834 0.016

-0.297 -2.316 0.041 0.178 5.399 0.000

-2.485 -4.384 0.001 -1.332 -2.661 0.022

0.206 2.917 0.014-0.666 -3.327 0.006

-0.205 -2.489 0.031 -0.257 2.951 0.013 0.360 5.835 0.001

0.157 2.811 0.0160.412 0.842

4.7491 0.004

0.346Note: The bounds test reports the computed F- statistic while Serial corr, ARCH and LR Symmetry report the respective p-values for tests for serial correlation, ARCH effects and the hypothesis that both exchange rate coefficients are equal (long-run symmetry).Source: Authors Computations

The results for the “two threshold case” using 30% and 70% quantile of exchange rate changes as thresholds are presented in Table 4.6. This decomposition allows us to distinguish between small and large rate changes. Again, as in the “one threshold case”, the evidence in favour of cointegration is very strong. The computed is 4.846, which is greater than the upper critical bound at the 1 percent significance level. Thus, the null hypothesis of no cointegration is rejected in favour of the alternative hypothesis of a stable long-run relationship. The overall fit is reasonable, with the adjusted of 0.423. The adjusted is larger than the ARDL, indicating superiority of the NARDL over the ARDL. A formal test of long-run symmetry is also conducted, and as can be seen from Table 4.6 the hypothesis of long-run symmetry (all exchange rate coefficients are equal) is rejected, implying existence of a non-linear relationship.

The coefficients of the lagged level regressors are significant and consistent with theory. The coefficient of large exchange rate changes is larger than the coefficient of small exchange rate changes, implying some degree of hysteresis. The exchange rate coefficients have got the expected positive signs, which imply a positive relationship between exports and the exchange rate. The relative price and a proxy for foreign income have the expected respective negative and positive coefficients, which are also significant.

Table 4.6: Results of the Non-linear Autoregressive Model, Two threshold case, (30%/70% quantile)Dep. Variable: Regressor Coefficient t-values p-values

1.845 2.828 0.041-0.465 -4.205 0.002

0.423 4.323 0.0010.345 2.833 0.0130.215 3.452 0.001

-0.297 -2.716 0.020 0.178 5.399 0.002

-2.485 -4.383 0.0030.409 2.917 0.0140.352 2.892 0.011

0.266 3.327 0.006 -0.205 -2.489 0.030

-0.257 -2.950 0.013 0.369 5.835 0.000

0.157 2.827 0.0160.423 0.6325

4.8462 0.00000.2699

Note: The bounds test reports the computed F- statistic while Serial corr, ARCH and LR Symmetry report the respective p-values for tests for serial correlation, ARCH effects and the hypothesis that both exchange rate coefficients are equal (long-run symmetry).Source: Authors Computations

4.3.2.3 Concluding RemarksThe focus of this section is to investigate the relationship between exchange rate changes and exports using a non-linear autoregressive distributed lag model recently developed by Shin et al. (2011). The empirical results indicate that assuming linearity in export demand functions might be too restrictive given the superiority of the NARDL over the ARDL. The long-run coefficients have the expected signs and are statistically significant. The “one threshold model”, which distinguishes exchange rate changes between appreciations and depreciations, indicates a non-linear relationship, and that exports respond more to appreciations than to depreciations. The results of the “two threshold model”, which distinguishes large and moderate exchange rate changes, indicate that large exchange rate changes have a more profound effect on exports than small exchange rate changes.

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Appendix 1: Evolution of Variables

Appendix 2a: SVAR Residual Serial Correlation LM Tests

Null Hypothesis: no serial correlation at lag order hSample: 1995M01 2014M08Included observations: 233Lags LM-Stat Prob

1  27.73844  0.83642  38.02477  0.37733  23.48897  0.94634  42.88467  0.19985  27.39987  0.84796  44.21539  0.16357  32.52450  0.63478  38.83820  0.3430

Appendix 2b: Stability Tests