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On the asymmetric effects of exchange rate changes on domestic production in Turkey

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Abstract

A few studies in the literature have used linear models to assess the impact of changes in the value of lira on Turkish domestic production. They have found no significant long-run effects. In this paper we separate lira appreciations from depreciations and engage in asymmetry analysis. Since the procedure requires using nonlinear models, we rely upon Shin et al.’s (Festschrift in honor of Peter Schmidt: econometric methods and applications. Springer, New York, pp 281–314, 2014) nonlinear ARDL approach and discover that indeed, the effects of changes in the real effective value of lira have asymmetric effects, both in the short run as well as in the long run. Indeed, in the long run we find that both lira depreciation and lira appreciation to have expansionary effects on domestic production in Turkey.

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Notes

  1. For the theory behind this topic see Krugman and Taylor (1978). It should be noted that a country’s net export will increase in the long run if the so called Marshal-Lerner condition holds, i.e., sum of import and export demand elasticities in absolute value exceeds unity. However, the short-run effects of a depreciation could be different. Indeed, in the short-run net exports could decline before improving in the long run, hence the J-curve effect (Bahmani-Oskooee and Zhang 2014).

  2. The same model and method are also applied to East European countries by Bahmani-Oskooee and Kutan (2008) which did not include Turkey.

  3. Other country specific time-series studies since Bahmani-Oskooee and Miteza’a 2003 review are: Kim and Ying (2007) for Asian countries, Narayan and Narayan (2007) for Fiji, Shahbaz et al. (2012) for Pakistan, Mejia-Reyes et al. (2010) for Latin American countries, Eltalla (2013) for Palestine, and Bahmani-Oskooee and Gelan (2013) for African countries.

  4. By deduction and a reference to Eq. (1) they are indeed equal.

  5. Indeed, under this method variables could be combination of I(0) and I(1) which are properties of almost all macro variables. Therefore, under this approach there is no need for pre unit root testing. Furthermore, the approach accounts for endogeniety of the variables by assuming the lagged values to serve as instruments (Bahmani-Oskooee and Hajileee 2010, p. 650). Pesaran et al. (2001, p. 299) alert us to this issue by writing “our approach is quiet general in the sense that we can use a flexible choice for the dynamic lag structure in …..as well as allowing for short-run feedbacks”.

  6. For some other applications of this approach see Halicioglu (2007), De Vita and Kyaw (2008), Dell’Anno and Halicioglu (2010), Chen and Chen (2012), Hajilee and Al-Nasser (2014), Bahmani-Oskooee and Zhang (2014), Bahmani-Oskooee et al. (2015), and Bahmani-Oskooee and Durmaz (2016).

  7. At any given period t, POSt is cumulative sum of all observations of ΔLnREXt till time t where negative changes have been replaced by zeros. By the same token, NEGt is cumulative sum of all observations of ΔLnREXt where positive changes have been replaced by zeros.

  8. See Shin et al. (2014, p. 291).

  9. For more details see Shin et al. (2014, pp. 292–293) and for some other application of partial sum approach see Apergis and Miller (2006), Delatte and Lopez-Villavicencio (2012), Verheyen (2013), Bahmani-Oskooee and Fariditavana (2016).

  10. Note that in Table 3 and other tables to follow, POS and NEG variables are replaced by POSREX and NEGREX to indicate that these partial sums are associated with the real exchange rate, REX. If these partial sums are associated with other variables such as those in Table 4, they will be identified so.

  11. Indeed, initially we introduced nonlinear adjustment of all variable by replacing each variable by their related partial sums of positive and negative changes. However, due to data limitation we were unable to estimate a model in which we had 12 exogenous variables (10 POS and NEG variables, one constant, and one dummy). Thus we decided to include nonlinear adjustment of government spending, money, and the exchange rate.

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Correspondence to Mohsen Bahmani-Oskooee.

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Valuable comments of two anonymous reviewers are greatly appreciated.

Appendix

Appendix

1.1 Data definition and sources

Annual data over the period 1974–2015 are used to carry out the empirical analysis.

The data come from the following sources:

  1. a.

    International Financial Statistics of the IMF.

  2. b.

    World Development Indicators of the World Bank (WB).

  3. c.

    Ministry of Finance of Turkey (MFT).

  4. d.

    Ministry of Employment and Social Security of Turkey (MESST).

1.2 Variables

Y:

Real gross domestic product per capita in Turkish Lira (TL). Nominal figures are deflated with Gross Domestic Product (GDP) deflator, source a

M:

Real broad money supply per capita in TL. Nominal figures are deflated by GDP deflator, sources a and b

G:

Consolidated government expenditures per capita in TL. Nominal figures are deflated by GDP deflator, source c and b

OIL:

Real price per barrel of crude oil in TL. Dollar prices are converted to TL and then deflated by CPI (Consumer Price Index) of Turkey, source a

W:

Annual minimum real wage index in TL. Nominal prices are deflated by CPI. Source: own calculations from the statistics of MESST. Note that the minimum wage regulation was put in force in 1974. Wages were set more than twice in some years. Thus, the data was adjusted accordingly

REX:

Real effective exchange rate index is based on CPI of 67 trading partners of Turkey. An increase in this index indicates appreciation of Turkish Lira against the basket of currencies of trading partners. Source: www.bruegel.org.

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Bahmani-Oskooee, M., Halicioglu, F. & Mohammadian, A. On the asymmetric effects of exchange rate changes on domestic production in Turkey. Econ Change Restruct 51, 97–112 (2018). https://doi.org/10.1007/s10644-017-9201-x

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  • DOI: https://doi.org/10.1007/s10644-017-9201-x

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