Abstract
As the trade volume of Turkey has tripled in the last decade, one observes a complementary shift in the trade orientation of the country from its conventional markets like the European Union to Asian and African markets. Among the alterative markets, the members of the Organization of Islamic Cooperation (OIC) have become particularly important in the market diversification policy of Turkey. In this respect, computing the actualized trade potential can not only account for the dynamic change in trade orientation of the country but also presents a guideline for policy makers and firms. On the other hand, since recent research in literature stated that estimations of potential trade through a single estimator (monotype estimation) lead to overestimations (or underestimations) which misguide policy makers; thus, this paper employs an estimator selection process. For that purpose, this study uses a gravity model estimated by multiple alternative estimators to assure the econometric credibility. This paper aims at (1) choosing the most adequate estimator possible for the case through an estimator selection process, (2) computing the trade potential of Turkey in the OIC market, and (3) revealing to what extent the trade potential has been actualized up to now.
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Notes
- 1.
It can be expected that Turkey has a differentiated goods based manufacturing market for exporting toward the OIC counties while they lack production complementarity for exporting toward Turkey.
- 2.
The lack of production complementarity and the relative similarity of resource endowments (except energy commodities) between Turkey and the OIC members argues against intraregional trade since the comparative advantage among countries in question is mainly in the same products. The lack of diversified products in manufactures has limited the trade opportunities in the course of time. Due to this fact Turkey that historically concentrated its trade flows mainly with the European countries.
- 3.
Matyas (1998) points out two major advantages of panel data: to increase degree of freedom, to correctly account for importing country effect.
- 4.
De Benedictis and Vicarelli (2004) estimates 143 cases using several estimators: OLS based estimation give %46 of untapped trade potential but using FEM-within and GMM estimators this reduced 24 and 3 %, respectively. Additionally, there is a great difference between static and dynamic estimators. Dynamic estimator gives a better result.
- 5.
\( DIST_{ijt} = Distance*\left| {\frac{{GDP_{it} - GDP_{jt} }}{{GDP_{it} + GDP_{jt} }}} \right| \). The distance variable is measured in kilometers between capitals and computed as GDP weighted for every time period.
- 6.
In Gravity type models time-fixed regressors like geographical or cultural distance, language and institutional (dummy) variables are widely used to analyse the impact of trade costs on bilateral trade. Panel data settings help avoid the inconsistency problem due to a correlation of the time-fixed regressors with the combined error term in the model. It is the major reason for using gravity models based on panel data in the literature on trade. Nevertheless, since estimated gravity models based on panel data are often static they only allow for contemporaneous effects of the regressors on trade while ignoring dynamics effects (De Grauwe and Skudelny 2000). In multi-country case cross-sectional time series data set, in bi-country case time-series data set allows to explain dynamic effects while estimating gravity model.
- 7.
“However, the expected value of the error term is a function of the regressors if the data are heteroscedastic- as usually happens with trade data […] Heteroskedasticity does not affect the parameter estimates; the coefficients should still be unbiased, but it biases the variance of the estimated parameters and, consequently, the t-values cannot be trusted” (Herrera 2012).
- 8.
Another problem observed in panel data is autocorrelation. To detect the presence of autocorrelation Wooldridge test is conducted. The null hypothesis that there is no autocorrelation is rejected for the whole models. The autocorrelation problem is solved through AR(1) method while the heteroskedasticity problem is solved through White’s cross section coefficient covariance method.
- 9.
However, in spite of fitted estimations the statistical significance of the dynamic panels is arguable: “If trade is a static process, the within estimator is consistent for a finite time dimension T and an infinite number of country-pairs N. But if trade is a dynamic process, the estimate of a dynamic panel […] with the inclusion of a lagged dependent variable is more complex. If country specific effects are unobserved, they are included in the error term; the introduction of the lagged dependent variable on the right hand side of the equation leads to correlation between the lagged dependent variable and the error term that (for a finite T and an infinite N) renders least square estimator biased and inconsistent. If time dimension T is fixed, the transformation needed to wipe out the country-pair fixed effects could not resolve the problem” (Baltagi 2001).
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Appendix A
Appendix A
Azerbaijan, Jordan, Afghanistan*, Albania, The United arab emirates, Indonesia, Uzbekistan, Uganda*, Iran, Pakistan, Bahrain, Brunei-Darussalam, Bangladesh, Benin, Burkina-Faso, Tajikistan, Turkey**, Turkmenistan, Chad, Togo, Tunisia, Algeria, Djibouti, Saudi Arabia, Senegal, Sudan, Syria, Suriname*, Sierra Leone*, Somalia, Iraq, Oman, Gabon, Gambia, Guyana*, Guinea, Guinea-Bissau*, Palestine, The Comoros, Kyrgyzstan, Cote D’ivoire*, Kuwait, Lebanon, Libya, Maldives, Mali, Malaysia, Egypt, Morocco, Mauritania*, Mozambique*, Niger, Nigeria, Yemen
*Marked countries are excluded due to missing data; **the source country
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Sorhun, E. (2014). Computation of the Potential Trade of Turkey in the OIC Market Through Estimator Selection Process. In: Dincer, H., Hacioglu, Ü. (eds) Globalization of Financial Institutions. Springer, Cham. https://doi.org/10.1007/978-3-319-01125-7_9
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