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Asymmetric effects of exchange rate changes on the Malaysia-EU trade: evidence from industry data

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Abstract

Owing to exchange rate depreciation, trade balance may deteriorate in the short run but improves in the long run, hence the J-curve phenomenon. Previous research has failed to find a strong support for this phenomenon, which could be due to assuming exchange rate changes to have symmetric effects on the trade balance or due to assuming linear adjustment. The asymmetry cointegration approach, which introduces nonlinearity into the model specification, may resolve a part of the problem if not all. Following a recent nonlinear approach to cointegration (i.e. NARDL), this research examines the phenomenon displayed in Malaysia-EU bilateral trade for each of the 63 industries that trade between the two regions. We find that exchange rate changes have significant short-run asymmetric effects on the trade balance of most industries. As expected, the nonlinear model and asymmetry cointegration provides more support for the J-curve.

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Notes

  1. Foreign trade ratio of Malaysia is 141.6 %, divided into 74.4 % for exports and 67.2 % for imports [European Commission (EC) 2013].

  2. EU is the fourth largest trading partner of Malaysia [Ministry of International Trade and Industry (MITI), 2012].

  3. To judge the significance of normalized coefficients we need to calculate their standard errors. These standard errors are calculated using the non-linear least square technique and the Delta method, which are both built into the Microfit statistical package used in this paper to produce the results.

  4. For details of normalization and how to calculate the standard error of the normalized coefficients see Bahmani-Oskooee and Fariditavana (2015) and for some other applications see Halicioglu (2007, 2013), Narayan et al. (2007), Wong and Tang (2008), De Vita and Kyaw (2008), Payne (2008), Chen and Chen (2012), Tayebi and Yazdani (2014), and Hajilee and Al-Nasser (2014).

  5. The partial sum concept is similar to generating the cumulative sum of a variable. The difference is that in generating the partial sum of positive changes, we replace the negative changes by zeros. Similarly, in generating the partial sum of the negative changes, we replace the positive changes by zeros.

  6. For some other applications of the nonlinear model and the partial sum concept see Apergis and Miller (2006), Delatte and Lopez-Villavicencio (2012), Verheyen (2013), Greenwood-Nimmo and Shin (2013), Atil et al. (2014), Bahmani-Oskooee and Fariditavana (2014), and Bahmani-Oskooee and Bahmani (2015).

  7. This critical value is at the 10 % significance level, where there are three exogenous variables (k = 3) in the model. It comes from Pesaran et al. (2001, Table CI, Case III, p. 300).

  8. For details of all these tests and a graphical presentation of CUSUM and CUSUMSQ tests see Bahmani-Oskooee and Fariditavana (Bahmani-Oskooee and Fariditavana 2015).

  9. On this issue we are following Shin et al (2014, p. 291) who recommend using k = 1 in both linear and nonlinear ARDL model when there is only one exogenous variable to begin with. It amounts to assuming k = 3 in our case because we begin with three exogenous variables in the linear model. As they argue, due to the dependence structure that exists between the partial sums, the true value of k lies between 3 and 4 in our case. Since k = 3 critical values are higher, we are safe to use higher values.

  10. For each industry i, we construct Malaysian bilateral trade with EU by combing the bilateral trade with 28 European economies (i.e. Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Croatia, Hungary, Ireland, Italy, Lithuania, Luxembourg, Latvia, Malta, Netherlands, Poland, Portugal, Romania, Sweden, Slovenia, Slovakia, Spain, the United Kingdom).

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Authors and Affiliations

  1. Center for Research on International Economics, University of Wisconsin-Milwaukee, Milwaukee, WI, USA

    Mohsen Bahmani-Oskooee

  2. Department of Economics, University of Wisconsin-Milwaukee, Milwaukee, WI, USA

    Mohsen Bahmani-Oskooee

  3. Department of Finance and Banking, University of Malaya, Kuala Lumpur, Malaysia

    Muhammad Aftab

Authors
  1. Mohsen Bahmani-Oskooee
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  2. Muhammad Aftab
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Corresponding author

Correspondence to Mohsen Bahmani-Oskooee.

Appendix: Data and sources

Appendix: Data and sources

This study uses monthly data over the period, May 2000–December 2013. All the data are obtained from the following three sources:

  1. (a)

    External Trade Statistics, Department of Statistics Malaysia,

  2. (b)

    Datastream, Thomson Reuters

  3. (c)

    International Financial Statistics (IFS), International Monetary Fund (IMF).

1.1 Variables

  • BoT i  = For each industry i, this is defined as the ratio of Malaysian imports from EUFootnote 10 over its exports to EU. The data on bilateral trade and each of 63 industries (HS 2-digit) are taken from source a.

  • IP ML = Malaysian Industrial production index is used as a measure of economic activity. The data originate from source b.

  • IP EU = European Industrial Production index, source b.

  • REX = Real bilateral exchange rate between euro and Malaysian ringgit, defined as \( REX_{t} = \frac{{(NEX_{t} )(CPI_{t}^{ML} )}}{{CPI_{t}^{EU} }} \) where NEX t is nominal bilateral exchange rate defined as number of euro per ringgit). CPI is used to measure price level. CPI data and NEX data come from source c.

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Bahmani-Oskooee, M., Aftab, M. Asymmetric effects of exchange rate changes on the Malaysia-EU trade: evidence from industry data. Empirica 44, 339–365 (2017). https://doi.org/10.1007/s10663-016-9324-8

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  • DOI: https://doi.org/10.1007/s10663-016-9324-8

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