estimating import and export demand elasticities for ...

ESTIMATING IMPORT AND EXPORT DEMAND ELASTICITIES FOR MAURITIUS AND SOUTH AFRICA SEEMA NARAYAN Royal Melbourne Institute of Technology University

PARESH KUMAR NARAYAN Deakin University In this paper, we re-estimate the import and the export demand functions for Mauritius and South Africa using time series data. We use the bounds tests for cointegration and find evidence of a long-run relationship between import demand, income and prices for both countries. Our long run elasticities reveal that domestic income and relative prices have significant effects on the import demand for both countries, with income being the most important determinant. Furthermore, we find that while South Africa’s export demand is not responsive to relative prices or income; for Mauritius income is statistically significant.

I.

Introduction

The literature on import and export demand is voluminous. A few studies have previously estimated import and export demand elasticities for South Africa and Mauritius. BahmaniOskooee (1998) used the Johansen and Juselius (JJ) (1990) technique for cointegration to estimate the long run (1973–1990) import demand function for South Africa and other countries. For South Africa, he found: (1) a long-run relationship between import demand, income and prices; and (2) domestic income and relative price elasticities of import demand to be elastic. Using the same technique, but excluding the nominal exchange rate, Bahmani-Oskooee and Niroomand (1998) re-estimated the import and export demand functions for South Africa and Mauritius, amongst other countries, over the 1960 to 1992 period. In the long run, the South African import demand was found to be inelastic to changes in income and relative price variables. Their export demand was found to be elastic to changes in relative prices but inelastic to income. Meanwhile, in the case of Mauritius, Bahmani-Oskooee and Niroomand (1998) found that both import and export demands were elastic with respect to income but close to unitary with respect to the relative price in the long-run. Senhadji (1998) used the Phillip-Hansen Fully Modified Ordinary Least Squares (FMOLS) estimator to derive the long-run elasticities for South African and Mauritius import and demand for the period 1960 to 1993. His import demand elasticities were somewhat larger than those reported by Bahmani-Oskooee and Niroomand (1998) but smaller than those estimated by Bahmani-Oskooee (1998). However, contrary to Bahmani-Oskooee (1998) and Senhadji (1998), Bahmani-Oskooee and Niroomand’s (1998) results indicate that imports are more responsive to relative prices than to income. doi: 10.1111/j.1467-8454.2010.00399.x Correspondence: Paresh Kumar Narayan, School of Accounting, Economics and Finance, Faculty of Business and Law, Deakin University, 221 Burwood Highway, Burwood, Vic 3125. [email protected]. © 2010 The Authors Australian Economic Papers © 2010 Blackwell Publishing Ltd/University of Adelaide and Flinders University

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Senhadji and Montenegro (1999) found exports were highly responsive to income for both South Africa and Mauritius. Bahmani-Oskooee and Niroomand (1998) reported similar results for Mauritius but differed in the case of South Africa – where they showed exports were elastic with respect to relative price and inelastic with respect to income. From these studies it is clear that there are conflicting results on the relationships emerging from the export and import demand models for South Africa and Mauritius. This motivated us to reinvestigate this relationship by using more robust and recent econometric techniques. Our choice of Mauritius and South Africa for empirical analysis is based on two factors: (1) for these two African countries, good quality data for a sufficient period of time is available for time series econometric analysis; and (2) traditionally, these two countries have attracted much interest as can be seen from the literature reviewed above. Our study allows us to compare our results with previous studies on these two countries, which is relevant given that we use the most recent and up-dated dataset compared with the extant literature on South Africa and Mauritius. Our study is as much as 15 years more recent than existing studies, which represents a significant update of the literature. Thus, this is the first contribution to the literature. The second contribution of this study to the literature is the use of recent advances in time series econometrics such as the bounds testing procedures to cointegration, developed by Pesaran and Shin (1995, 1999), Pesaran et al. (2001) and Banerjee et al. (1998). We use the error correction mechanism (ECM) test for cointegration developed by Banerjee et al. (1998) and extended by Pesaran et al. (2001), who termed it bounds t-test, as a supplementary test for cointegration. The bounds t-test has not been previously used in the export and import demand literature, while the bounds F-testing approach has not been previously used to examine evidence for cointegration in export and import demand models for Mauritius and South Africa. Using both the F-test and t-test to examine the possibility of a long-run relationship will ensure that the results are robust. In addition, we use the autoregressive distributed lag (ARDL) model to estimate the long run and short run elasticities of the determinants of import demand. Our third contribution is as follows. One feature of existing studies on export and import demand functions for Mauritius and South Africa is that they have been based on relatively small sample sizes: Bahmani-Oskooee (1998) used quarterly data for the period 1973–1990 and Bahmani-Oskooee and Niroomand (1998) used data for the period 1960–1992. They subjected these finite sample sizes to the JJ (1990) Maximum Likelihood (ML) test for cointegration. It is now widely known that the JJ test does not provide robust results with small sample sizes. Further, Shiller and Perron (1985) and Hakkio and Rush (1991) contend that increasing the number of observations by using monthly or quarterly data does not add any robustness to the results in cointegration analysis, because the concern in such analysis is the length of the sample period. On the other hand, an important advantage of the bounds t-test and F-test approaches under the ARDL framework is that they have better properties for small samples than does the JJ test.1 It follows, then, that the aims of the paper are two-fold: (1) to investigate whether a long-run relationship exists between imports, prices and income for Mauritius and South Africa, using new cointegration techniques; and (2) to estimate the impact of income and prices on export demand for Mauritius and South Africa. The rest of the paper is organised as follows. In the next section, we present the import and export demand models. In Section III, we discuss the results, and in the final section we provide some concluding comments. 1

As a result of the small sample properties of the ARDL technique, several studies have applied this approach (see, inter alia, Narayan (2004), Narayan (2005a,b,c), Narayan and Narayan (2004a,b,c), Narayan and Smyth (2005)). © 2010 The Authors Australian Economic Papers © 2010 Blackwell Publishing Ltd/University of Adelaide and Flinders University

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II.

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Import and Export Demand Models

Following previous studies (see Narayan & Narayan, 2005a,b), our long-run import demand model takes the following form:

ln M t = α + β1 ln MRPt + β 2 ln Yt + ε t

(1)

where, at period t, lnMt is the log of imports of goods and services (real imports); lnMRPt is the log of the relative price variable, calculated as a ratio of import price index to domestic prices (here, domestic prices are proxied by the consumer price index); and lnYt is the log of the real gross domestic product. a is the constant; et is the error term; and b1 and b2 are the price and the income elasticities, respectively. Consistent with demand theory, imports are negatively related to relative prices and positively related to income; hence, it is expected that b1 < 0 and b2 > 0. The export demand model, on the other hand, takes the following form:

ln X t = α + γ 1 ln XRPt + γ 2 lnWYt + ε t

(2)

where, at period t, lnXt is the log of exports of goods and services (real exports); lnXRPt is the log of the relative price variable, calculated as a ratio of export prices to world export prices; and lnWYt is the log of world output. a is the constant; et is the error term; and g1 and g2 are the price and the income elasticities, respectively. Exports are negatively related to relative prices and positively related to income; hence, it is expected that g1 < 0 and g2 > 0. The absolute values of import demand and export demand price elasticities – |g1 + b1| – can be used to measure the Marshall Lerner condition. This condition suggests that if |g1 + b1| > 1 then devaluation could effectively improve the trade balance. It is worth mentioning that the measure of the price variable (XRP) used here is same as Senhadji and Montenegro (1999). For the income variable, some studies, (see Narayan & Narayan, 2004b; Senhadji & Montenegro, 1999), used trade weighted average of trading partners’ income as a proxy. Since several industrial countries were/are key trading partner countries of South Africa and Mauritius, we use the index of industrial production of industrialissed countries to capture the effect of income in the export demand model. Bahmani-Oskooee and Niroomand (1998) used industrial production index as a proxy of income. These models are estimated using annual time series data, covering the period 1969 to 2008 and 1960–2005 for Mauritius and South Africa, respectively. The data are sourced from the International Monetary Fund’s International Financial Statistics. The bounds F-test and t-test, under the ARDL framework, are used to test for the existence of any long-run relationships, while the ARDL technique is used to estimate the short and long run elasticities.2 The bounds testing procedure involves two stages. The first stage is to establish the existence of a long-run relationship. Once a long-run relationship has been established, a two-step procedure is used in estimating the long-run relationship. An initial investigation of the existence of a long-run relationship predicted by theory among the variables in question (see equation (3) below) is preceded by an estimation of the short-run and long run parameters. Suppose that with respect to equation (1), theory predicts that there is a long-run relationship among lnMt, lnYt and lnMRPt. Without having any prior information about the direction of the long-run relationship among the variables, the following unrestricted error correction (EC) regressions are estimated (for equation (1)), taking each of the variables in turn as a dependent variable:

2

The methodology on the ARDL estimator applied to import demand can be found in Narayan and Narayan (2005a). © 2010 The Authors Australian Economic Papers © 2010 Blackwell Publishing Ltd/University of Adelaide and Flinders University

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n

n

n

i =1

i=0

i=0

Δ ln M t = a0 M + ∑ biM Δ ln M t − i + ∑ ciM Δ ln Yt − i + ∑ diM Δ ln MRPt − i + λ1M ln M t −1 + λ 2 M ln Yt −1 + λ3 M ln MRPt −1 + ε1t n

n

n

i =1

i=0

i=0

(3a)

Δ ln Yt = a0Y + ∑ biY Δ ln Yt − i + ∑ ciY Δ ln M t − i + ∑ diY Δ ln MRPt − i + λ1Y Δ ln Yt −1 + λ 2Y Δ ln M t −1 + λ3Y ln MRPt −1 + ε 2t n

n

n

i =1

i=0

i=0

(3b)

Δ ln MRPt = a0 MRP + ∑ biMRP Δ ln MRPt − i + ∑ ciMRP Δ ln Yt − i + ∑ diMRP Δ ln M t − i + λ1MRP Δ ln MRPt −1 + λ 2 MRP Δ ln M t −1 + λ3 MRP ln Yt −1 + ε 3t

(3c)

For equation (2), the unrestricted EC regressions are as follows: n

n

n

i =1

i=0

i=0

Δ ln X t = a0 X + ∑ biX Δ ln X t − i + ∑ ciX Δ lnWYt − i + ∑ diX Δ ln XRPt − i + λ1 X ln X t −1 + λ 2 X lnWYt −1 + λ3 X ln XRPt −1 + ε1t n

n

n

i =1

i=0

i=0

(4a)

Δ lnWYt = a0WY + ∑ biWY Δ lnWYt − i + ∑ ciWY Δ ln X t − i + ∑ diWY Δ ln XRPt − i + λ1WY Δ lnWYt −1 + λ 2WY Δ ln X t −1 + λ3WY ln XRPt −1 + ε 2t n

n

n

i =1

i=0

i=0

(4b)

Δ ln XRPt = a0 XRP + ∑ biXRP Δ ln XRPt − i + ∑ ciXRP Δ ln Yt − i + ∑ diXRP Δ ln X t − i + λ1 XRP Δ ln XRPt −1 + λ 2 XRP Δ ln X t −1 + λ3 XRP lnWYt −1 + ε 3t

(4c)

When a long-run relationship exists, the F test indicates which variable should be normalised. The null hypothesis for no cointegration amongst the variables in equation (3a) is (H0 : l1M = l2M = l3M = 0) denoted by FM(M|MRP,Y) against the alternative (H1 : l1M ⫽ l2M ⫽ l3M ⫽ 0). Similarly, the null hypothesis for testing the ‘nonexistence of a long run relationship’ in equation (3b) is denoted by FY(Y|M,MRP); for equation (3c) the F test for testing the null hypothesis is denoted by FMRP(MRP|M,Y). For equations (4a), (4b) and (4c), the F test for testing the null hypothesis is denoted by FX(X|WY,XRP); FWY(WY|X,XRR); and FXRP(XRP|X,WY), respectively. These hypotheses can be examined using the F statistic, which has a non-standard distribution, which depends upon: (i) the sample size; (ii) whether variables included in the model are I(0) or I(1); (iii) the number of regressors; and (iv) whether the model contains an intercept and/or a trend. Critical values are reported by Pesaran and Pesaran (1997) and Pesaran et al. (2001). However, these critical values are generated for sample sizes of 500 observations and 1000 observations and 20,000 and 40,000 replications respectively. Given the relatively small sample size in our study (40 observations and 47 observations for Mauritius and South Africa, respectively) we make use of approximate critical values of the bounds F-test for relatively small sample sizes, which are different from critical values when there are very many observations as considered in Pesaran and Pesaran (1997) and Pesaran et al. (2001). See Appendix A in Narayan (2005a) for a new set of critical values of the bounds F-test for sample sizes ranging 30–80.3 Two sets of critical values are available: One set refers to the I(1) series and the other for the I(0) series. Here, critical values for the I(1) series are referred to as the upper bound critical values while the critical values for the I(0) series are referred to as the lower bound critical values. If the computed F statistics falls outside the critical bounds, a conclusive decision can be made regarding cointegration without knowing the order of integration of the regressors. For instance, if the empirical analysis shows that the estimated FM(.) is higher than the upper bound of the 3

See also Narayan (2004).

© 2010 The Authors Australian Economic Papers © 2010 Blackwell Publishing Ltd/University of Adelaide and Flinders University

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critical values then the null hypothesis of no cointegration is rejected. Once a long-run relationship has been established, in the second stage, a further two step procedure to estimate the model is carried out. First, we select the orders of the lags in the ARDL model by using the Schwartz Bayesian Criteria (SBC), which is calculated as follows:

log n ⎞ SBCσ = log (σ 2 ) − ⎛ p ⎝ n ⎠ Here, σ is the estimated standard error of the regression, p is the dimension and n is the sample size. According to the SBC, a model is chosen if it has the lowest SBCs value. In the second step we estimate the selected model by the ordinary least squares technique. Meanwhile, the bounds t-test is based on the significance of the one period lagged dependent variable. The critical values for the t-test are derived from Pesaran et al. (2001). They provide a bounds procedure whereby the asymptotic distribution of their statistics is obtained for cases in which all regressors are purely I(1) as well as when the regressors are purely I(0) or mutually cointegrated.

III.

Em p i r i c a l Re s u lt s

a) Cointegration Using the bounds technique, we test for the presence of long-run relationships. The calculated F-statistics are compared against the critical values for our sample size of T = 40 and T = 47 for Mauritius and South Africa, respectively. For equation (2a), the calculated F-statistic FM(M|MRP,Y) is higher than the upper bound critical value at the one per cent level for Mauritius and at the five per cent level for South Africa. Thus, the null hypothesis of no cointegration is rejected for both these countries. The t-test for cointegration also supports the existence of a cointegration relationship in that the calculated t-statistics are greater (in absolute terms) than the ten per cent critical values (Table I).

b) Long-run and short-run elasticities Once we established that a long-run cointegration relationship existed for South Africa and Mauritius, equation (1) was estimated using the following ARDL (m,n,q) specification for the export demand (equation (5)) and the import demand (equation (6)) models: m

n

q

i =1

i=0

i=0

m

n

q

i =1

i=0

i=0

ln M t = α 0 + ∑ α1 ln M t − i + ∑ α 2 ln MRPt − i + ∑ α 3 ln Yt − i + μt ln X t = α1 + ∑ b1 ln X t − i + ∑ b2 ln XRPt − i + ∑ b3 lnWYt − i + μt

(5) (6)

For each model, given the use of annual data and small sample size (40 observations for Mauritius and 46 observations for South Africa) a maximum of two lags was used, such that imax = 2. Given the small sample size, more than two lags can create problems related to degrees of freedom. The optimal lag length is based on the SBC. The results from the long run models, using the ARDL and maximum likelihood estimators (ML), are presented in Table II. With respect to the import demand model, we find that relative prices have a negative impact on imports and this relationship is statistically significant. According to the ARDL estimator, ceteris paribus, a one per cent increase in relative prices leads to a fall in import demand by 0.7 © 2010 The Authors Australian Economic Papers © 2010 Blackwell Publishing Ltd/University of Adelaide and Flinders University

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Table I Test for cointegration relationships 1. F-test Critical value bounds of the F statistic: intercept and no trend 90 per cent level K=2 T = 40 T = 46

I(0) 2.880 2.865

I(1) 3.653 3.608

95 per cent level I(0) 3.500 3.468

97.5 per cent level

I(1) 4.373 4.295

I(0) 5.048 4.920

I(1) 6.053 5.975

Calculated F-statistic Mauritius 9.5411 2.1161 0.1192 6.6210 2.5099 3.0029

FM(M|RP,Y) FRP(RP|M,Y) FY(Y|RP,M) FX(X|RP,Yw) FRP(RP|X,Yw) FYw (Yw RP , X )

South Africa 5.3156 3.2210 1.9772 4.9661 3.1111 2.6771

2. Bounds t-test Critical value: 5 per cent t-statistic Mauritius South Africa

Critical value: 10 per cent

I(0)

I(1)

I(0)

I(1)

-1.95

-3.02

-1.62

-2.68

Imports = -2.99 Exports = 3.11 Imports = -3.05 Exports = 3.31

Notes: Critical values for the bounds F-test are extracted from Narayan (2005a) while critical values for the bounds t-test are extracted from Pesaran et al. (2001).

per cent and one per cent for Mauritius and South Africa, respectively. Meanwhile, the ML estimator reveals that a one per cent increase in relative prices leads to a fall in import demand by 0.7 per cent and 0.8 per cent for Mauritius and South Africa, respectively. Domestic income, on the other hand, is positively related to import demand. According to the ARDL estimator, ceteris paribus, a one per cent increase in domestic income leads to 1.3 per cent increase in imports in the case of Mauritius and a 1.6 per cent increase in South African imports. Meanwhile, the ML estimator reveals that a one per cent increase in domestic income leads to a 1.3 per cent and a 1.7 per cent increase in import demand for Mauritius and South Africa, respectively. On the other hand, industrial production index of industrialised countries and relative price are insignificant determinants for the South African export demand but significant in the case of Mauritius exports. According to the ARDL estimator, ceteris paribus, a one per cent increase in relative prices leads to a fall in export demand by 0.7 per cent for Mauritius. Meanwhile, the ML estimator reveals that a one per cent increase in relative prices leads to an increase in export demand by 1.0 per cent and 0.5 per cent for Mauritius and South Africa, respectively. Movements in advanced countries’ income are positively related to export demand. Other things being equal, a one per cent increase in advanced countries’ income leads to a 0.3 per cent increase in exports from Mauritius. Meanwhile, the ML estimator reveals that a one per cent increase in domestic income leads to a 0.3 per cent and a 1.3 per cent increase in import demand for Mauritius and South Africa, respectively. The short-run results are presented in Table III. The final model is selected using the SBC. The dynamics of the equations show that changes in relative prices and domestic income have significant impacts on imports of the two countries. We also find evidence suggesting that there © 2010 The Authors Australian Economic Papers © 2010 Blackwell Publishing Ltd/University of Adelaide and Flinders University

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Table II Long run results Import demand Mauritius ARDL (2,2,1) Constant lnMRP lnY South Africa ARDL (1,0,1) Constant lnMRP lnY

ARDL coefficients (standard errors)

ML coefficients (standard errors)

1.3426*** (0.3760) -0.7316** (0.2857) 1.2721*** (0.3760)

-1.8017*** (-0.3197) -0.7114** (0.2695) 1.3336*** (0.0736)

-3.3290*** (0.6141) -0.9973*** (0.1927) 1.6464*** (0.1416)

-3.3194*** (0.3886) -0.8379*** (0.1399) 1.6516*** (0.0878)

-10.6956*** (2.1070) -0.1889 (0.1743) 3.3319*** (0.4672)

4.3726*** (0.2335) 1.0438*** (0.1039) 0.3094 (0.1733)

Export demand Mauritius ARDL (1,2,0) Constant lnMRP lnWY South Africa ARDL (1,0,0) Constant lnXRP lnWY

-0.9026 (7.295) 0.1136 (0.4114) 1.2360 (1.6049)

3.3598 (0.3417) 0.4937*** (0.1132) 1.3077*** (0.2510)

Note: ** (***) significance levels at 5% and 1% respectively.

is a faster response to changes in real income than to relative prices. The error correction term, ECMt-1, which measures the speed at which import demand adjusts to changes in the explanatory variables before converging to their equilibrium levels, is negative and similar in size for both countries. In most cases, it is statistically significant, ensuring that the series is non-explosive and that long-run equilibrium is attainable. We find that the ECMt-1 is insignificant in one instance, that is, for South African export demand model. The coefficient of –0.30 for both countries’ import demand model implies that a deviation from the long-run level of imports this period is corrected by about 30 per cent in the next period. For Mauritius export demand, the ECMt-1 coefficient suggests that a deviation from the long-run level of exports this period is corrected by about 36 per cent in the next period. Having presented our results, it is worth comparing it with those from previous studies on Mauritius and South Africa. These results are presented in Tables IVa and IVb. Our import demand results, based on the ARDL estimator, are somewhat different from those obtained by previous studies using the ML and the fully modified ordinary least squares estimators. On the © 2010 The Authors Australian Economic Papers © 2010 Blackwell Publishing Ltd/University of Adelaide and Flinders University

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Table III Error correction models of import demand Variables Import demand Constant DlnMt-1 DlnRPt DlnRPt-1 DlnYt DlnYt-1 ECMt-1 Export demand Constant

Mauritius ARDL (2, 2,1)

South Africa ARDL (1, 0,1)

-0.4158*** (0.1853) 0.5694*** (0.1223) -0.6791*** (0.1413) 0.3942** (0.1421) 1.1421*** (0.2936) – -0.3097*** (0.0855)

-1.0210*** (0.1775) – -0.3059*** (0.0576) – 4.0096*** (0.3538) – -0.3067*** (0.0495)

-3.8972*** (0.0957) -0.4001*** (0.0947) – 1.2141*** (0.3501) – -0.3644*** (0.0957)

DlnRPt DlnRPt-1 DlnWYt DlnWYt-1 ECMt-1

-0.1092 (0.8707) 0.0137 (0.0511) – 0.1495 (0.1979) – -0.1210 (0.0835)

Notes: *(**)*** significance at 10%, 5% and 1% levels respectively. The figures in parenthesis are the standard errors. Note also that the different short-run models for Mauritius and South Africa is purely to the different long-run ARDL models. For instance, in the case of Mauritius’s import demand the SBC reveals an ARDL (1,2,2) model and for South Africa’s import demand the SBC reveals an ARDL (1,0,0) model.

Table IVa Results from previous studies on South African and Mauritius import demand Bahmani-Oskooee (1998) Variables lnY lnMRP

Bahmani-Oskooee and Noormand (1998)

Senhadji (1998)

Present study – from Table II

South Africa

South Africa

Mauritius

South Africa

Mauritius

South Africa

Mauritius

2.174 (2.7827) -1.37*** (0.2009)

0.43*** (0.1062) -0.53*** (0.0814)

1.05*** (0.1200) -0.93*** (0.1195)

0.67*** (0.0585) -1.00*** (0.1792)

2.25 (2.1847) -2.78 (5.2428)

1.65*** (0.1416) -1.00** (0.1927)

1.27*** (0.0830) -0.73** (0.2857)

Notes: standard errors are reported in parenthesis. ** (***) denote statistical significance, based on the t-test statistics, at the 5% and 1% levels respectively.

import demand model, income elasticity, for instance, for South Africa our elasticity is 1.65, while others have found it to be 2.17, 0.43 and 0.67. For Mauritius, we found the income elasticity to be 1.27, while others have found it to be 1.05 and 2.25. Similarly, our coefficient on relative price suggest that import demand is unitary for South Africa and relatively inelastic at (0.73) for Mauritius. However, others have found it to be –0.93 and –2.78 for Mauritius and –0.53 and –1.0 for South Africa. © 2010 The Authors Australian Economic Papers © 2010 Blackwell Publishing Ltd/University of Adelaide and Flinders University

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Table IVb Results from previous studies on South African and Mauritius export demand Bahmani-Oskooee and Noormand (1998) Variables lnWY lnXRP

Senhadji and Montenegro (1999)

Present study, from Table II

South Africa

Mauritius

South Africa Coef. (t-stat)

Mauritius Coef. (t-stat)

South Africa Coef. (std. error)

Mauritius Coef. (std. error)

-0.41*** (0.0466) -1.38*** (0.0945)

3.35*** (0.1982) -0.86*** (0.0541)

0.66*** (0.0639) -0.51*** (0.1131)

3.17** (1.3905) -1.92 (2.0000)

0.11 (0.4114) 1.24 (0.16049)

1.27*** (0.0830) -0.73** (0.2857)

Notes: standard errors are reported in parenthesis. ** (***) denote statistical significance, based on the t-test statistics, at the 5% and 1% levels, respectively.

Table V Results of goodness of fit and diagnostic tests for the error correction model presented in Table III Goodness of fit and diagnostics tests Import demand model R2 s c2Auto(1) c2Norm(2) c2ARCH(1) c2White(10 and 6) c2RESET(1) FForecast(6,19) Export Demand Model R2 s c2Auto(1) c2Norm(2) c2ARCH(1) c2White(10 and 6) c2RESET(2) FForecast(k,n)

Mauritius

South Africa

0.7176 0.0616 4.8620 0.0452 0.0042

0.8331 0.0442 0.1265 0.8015 0.0005

7.0704

0.0273

0.5482 0.0759 0.1198 0.7603 5.0662

0.1162 0.0470 0.0558 0.2944 0.3077

0.0517

0.7805

Notes: Where s is the standard error of the regression; c2Auto(2) is the Breusch-Godfrey LM test for autocorrelation; c2Norm(2) is the Jarque-Bera normality test; c2RESET(2) is the Ramsey test for omitted variables/finctional form; c2White(10) and (6) is the White test for heteroskedasticity; FForecast(6,19) is the Chow predictive failure test (when calculating this test, 1995 was chosen as the starting point for forecasting). Critical values for c2(2) = 5.99 and at the 5% level.

In order to test the reliability of the two error correction models, a number of diagnostic tests, including tests of autocorrelation, normality and heteroskedasticity in the error term, stability and accuracy of the model were applied (see Table V). We found no evidence of autocorrelation in the disturbance of the error term. The ARCH tests suggest the errors are homoskedastic and independent of the regressors. The model passes the Jarque-Bera normality tests suggesting that the errors are normally distributed. The RESET test indicates that the model is correctly specified, while the F-forecast test indicates the predictive power/accuracy of the model. Finally, the adjusted © 2010 The Authors Australian Economic Papers © 2010 Blackwell Publishing Ltd/University of Adelaide and Flinders University

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R-squared of the Mauritius and South African import demand model are 0.67 and 0.85, respectively. Hence, it is reasonable to claim that the two models have a good statistical fit.4

I V.

Conclusion

The objective of this paper was to re-estimate the import and export demand elasticities for Mauritius and South Africa using recent developments in time series econometric techniques. The reason for undertaking this study was the conflicting results emerging from previous attempts to estimate import and export demand elasticities. We notice that previous techniques – the Johansen and Juselius technique for cointegration (and the ML estimator), in particular – are not robust in finite samples. In this light, we employed two recent techniques – namely the bounds t-test and the bounds F-test, which have been shown to produce reliable and robust results in small samples – to search for cointegration. Moreover, we used the ARDL estimator to calculate the long run and short run elasticities. The bounds tests indicated that the import and export demand for Mauritius and South Africa were cointegrated with respect to price and income. By normalising on import demand, we estimated the long-run elasticities associated with import demand. We found a significant long-run relationship between import demand, relative prices and domestic income for both countries. The ARDL estimator revealed that domestic income had the most impact on import demand. Import demand functions for both countries also had a significant dynamic relationship. Furthermore, by normalising on export demand, we estimated the long-run elasticities associated with the export demand model. Based on the ARDL estimator, we found no evidence of a significant relationship between relative prices and export demand for the two African countries. Income was found to be significant only in the case of Mauritius. There are some important policy implications emerging from our empirical results. First, it is clear that while prices play an important role in the determination of imports for both Mauritius and South Africa, they do not seem to have any significant influence on exports in these countries. This means that the Marshall-Lerner condition does not hold in the case of both countries. In other words, devaluation will not be able to improve the external imbalances of these two countries. The inelastic response of the price variable in the case of both countries reflects the fact that a one per cent increase in prices will lead to a less than one per cent decrease in import demand, implying that both countries will have a higher import bill, which is likely to contribute to current account deficits. If the aim of policy makers is to maintain a sustainable current account deficit then particular emphasis needs to be placed on import price movements. To this end, while import prices are beyond the control of policymakers, inflation can be kept at favourable levels by prudent use of the monetary policies. Second, in the long-run growth in income has a significant and elastic impact on import demand for South Africa and Mauritius. This suggests that a one per cent growth in income will lead to a more than one per cent growth in import demand. If import growth outweighs 4

In addition, we are explicitly concerned about the stability of the long run import demand, which has important implications for the validity of the empirical results. Most previous studies, including those on Mauritius and South Africa, have presumed that the relationship is stable. Whether this is true is purely an empirical question; hence, there is no reason to believe a priori that the relative importance of factors influencing the relationship between imports, prices and income have remained unchanged. To ascertain parameter stability, we apply the Hansen (1992) stability test and find that the import demand models for both countries are stable. © 2010 The Authors Australian Economic Papers © 2010 Blackwell Publishing Ltd/University of Adelaide and Flinders University

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export growth, as has been the case for Mauritius and South Africa in most of the years during the sample period in this study, then balance of payment is likely to deteriorate. Given the elastic response of imports to an increase in income for both these countries, implications on current account deficits are likely to be a matter of concern for policy makers in South Africa and Mauritius. However, if imports consist mainly of capital goods used to generate exports and export-led growth then an appreciation of the currency can alleviate problems of balance of payment deficits.

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© 2010 The Authors Australian Economic Papers © 2010 Blackwell Publishing Ltd/University of Adelaide and Flinders University