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The Turkey-US commodity trade and the asymmetric J-curve

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Abstract

Unlike past studies, recent studies try to separate currency appreciations from depreciations and show that indeed, the effects of exchange rate changes on the trade balance are asymmetric. We add to this literature by considering the response of the trade balance of 45 industries that trade between Turkey and the USA We find that the real lira-dollar rate has short-run asymmetric effects in 28 out of 45 industries. Short-run asymmetric effects translate into the long-run asymmetric effects in only 13 industries. While our findings are industry specific, additional analysis revealed that in some industries while depreciations had significant effects, appreciations did not, in some other industries the opposite was true. Such findings were hidden by the estimates of past traditional linear models.

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Notes

  1. For studies related to other countries see the review article by Bahmani-Oskooee and Hegerty (2010).

  2. For a detailed review of the Turkish studies and their main features see Bahmani-Oskooee and Durmaz (2019).

  3. Bahmani-Oskooee and Durmza (2019, Footnote 14).

  4. Bahmani-Oskooee and Fariditavana (2015, 2016) argued that since traders’ reactions and expectations could be different to currency depreciations versus appreciations, trade flows could respond asymmetrically to exchange rate changes. Indeed, before them Bussiere (2013) showed that import and export prices react to exchange rate changes asymmetrically which could imply that trade flows and eventually trade balance also reacts asymmetrically to exchange rate changes.

  5. Defining the trade balance as a ratio not only allows us to express the model in logarithmic form, but also the ratio is unit free (Bahmani-Oskooee 1991).

  6. Note that Bahmani-Oskooee (2019) has demonstrated that the t test in this context is equivalent to testing the significance of lagged error-correction term in Engle and Granger (1987) specification.

  7. For some other application of these and other nonlinear models see Delatte and Lopez-Villavicencio (2012), Bussiere (2013), Wimanda (2014), McFarlane et al. (2014), Gogas and Pragidis (2015), Baghestani and Kherfi (2015), Al-Shayeb and Hatemi-J (2016), Lima et al. (2016), Aftab et al. (2017), Gregoriou (2017), Nusair (2012, 2017), Arize et al. (2017), Olaniyi (2019), and Istiak and Alam (2019).

  8. Our findings of support for the J-curve effect in 24 industries using the linear model indeed supports our approach of disaggregation by partners since Durmaz (2015) who only estimated the linear model but used aggregate data between an industry and rest of the world, found support for the J-curve in 11 industries.

  9. Note that in two industries coded 21 and 41 the estimate attached to LnREX is significantly negative and meaningful. These must be industries (though small) in which import demands are inelastic (Bahmani-Oskooee and Aftab 2017).

  10. Note that stable estimates are indicated by “S” and unstable ones by “U” in the two columns headed by CUSUM and CUSUMSQ.

  11. The main reason for presenting the estimates from both models is as follows. Suppose the exchange rate carries a significant coefficient of 0.6 in the symmetric model. This means a 1% depreciation improves the trade balance by 0.6% and a 1% appreciation hurts the trade balance by 0.6%. The asymmetric model may show it is only true for depreciation but not for appreciation or the other way around, e.g. industry coded 15. Or even the asymmetric model show both depreciation and appreciation have significant effects, their effects could be different in size, e.g. industry 28.

  12. Our finding of asymmetric J-curve in 25 industries that trade between Turkey and the USA is somewhat higher than the support for the asymmetric J-curve in 20 industries that trade between Turkey and EU by Bahmani-Oskooee and Durmaz (2019), again supporting disaggregation by trading partners. All sorts of industries, i.e. large versus small, agriculture versus technology, and durable vs. nondurables included in both studies.

  13. Data permitting, future research should concentrate and classify industries into those that use more intermediate goods versus those that use less.

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Authors and Affiliations

  1. The Center for Research in International Economics and Department of Economics, The University of Wisconsin-Milwaukee, Milwaukee, USA

    Mohsen Bahmani-Oskooee

  2. Department of Economics, Karabuk University, Karabuk, Turkey

    Huseyin Karamelikli

Authors
  1. Mohsen Bahmani-Oskooee
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  2. Huseyin Karamelikli
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Correspondence to Mohsen Bahmani-Oskooee.

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Appendix

Appendix

1.1 Data definition and source

Monthly data over the period January 2003–October 2018 are used in the empirical analysis. The data come from the following sources:

  1. A.

    Turkish Statistical Institute (http://www.turkstat.gov.tr).

  2. B.

    International Financial statistics (IFS).

Variables:

TBi = Turkish trade balance with the USA for commodity or industry ‘i’ defined as Turkish export of commodity ‘i’ to the USA divided by Turkish imports of commodity ‘i’ from the USA (Source: A).

YTR = Turkey’s aggregate output as measured by an index of industrial production since this is the only measure available at monthly frequency. (Source: B).

YUS = US aggregate output as measured by an index of industrial production. (Source: B).

REX = The real bilateral exchange rate of the US dollar against Turkish Lira. It is defined as REX = (PUS. NEX/PTR) where NEX is the nominal exchange rate defined as number of lira per USD. Thus, a decline in REX reflects a real depreciation of the US dollar. Both price levels are measured by CPI. All data come from source B (Fig. 1).

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Bahmani-Oskooee, M., Karamelikli, H. The Turkey-US commodity trade and the asymmetric J-curve. Econ Change Restruct 54, 943–973 (2021). https://doi.org/10.1007/s10644-020-09298-1

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  • DOI: https://doi.org/10.1007/s10644-020-09298-1

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