Purpose – The purpose of this paper is to examine the J-Curve effect in Turkey at the industry level. Design/methodology/approach – In order to find the long-run and short-run effects, 58 industries (by Standard International Trade Classification Rev.3) have been identified by using monthly data that covers the periods from January 1990 to December 2012. Present study employs bounds testing procedure, developed by Pesaran and Shin (1999) and Pesaran et al. (2001). Findings – Although results indicate a positive satisfactory effect of real depreciation of lira in 13 industries, the J-Curve effect is detected in only 13 industries. Originality/value – The present study is one of the first studies to analyze the J-Curve effect at the industry level on Turkey. In addition to being one of the first studies, it will be an invaluable addition to the J-Curve literature.

This paper investigates the stochastic nature of the unemployment rate allowing for cross-section dependence from a panel of US state-level data. We first employ the PANIC method to identify the common and idiosyncratic components. Powerful recursive mean adjustment (RMA) methods are used to test for unit roots. We find significant evidence of a nonstationary common component when the data from the most recent recession are included.Even when stationarity is empirically supported, the bias-corrected half-life of the common component appears very long, casting doubt on the usefulness of the natural rate hypothesis.

Purpose In the last decade, foreign direct investment (FDI) flows have increased dramatically in the world, especially in the emerging economies. Some of these countries make changes in their market conditions that will improve the civil rights and liberties to attract better FDI flows. The purpose of this paper is to test the linkage between democracy and FDI flows to Turkey. Design/methodology/approach The present study employs a bounds testing procedure developed (Pesaran et al., 2001) for cointegration analysis on six different long-run models with selected determinants of FDIs with yearly data from 1977 to 2011. Findings The intuition the paper empirically provides how improvements in democracy have a significant positive impact on FDI flows to Turkey. The results may also put forward that, in the long run, FDI inflows will have spillover effects in Turkey’s economy. Research limitations/implications Although one drawback in the study is having a small sample size of 35 observations, estimating six different long-run models is one way to overcome it. Thus presented results may be in short of simplification for some readers. This, however, opens an opportunity for future studies to further the proposal by employing in different models and/or longer data sets if possible. Practical implications A stable government policies, more civil freedom, and sustained institution politics should not be ignored in Turkey given its geopolitical location. Originality/value This paper satisfies the established need to study of democracy and FDI flows link is necessary in an emerging market such as Turkey.

Regional Trade Agreements (RTAs) have been advocated as one way of securing trade liberalization by the IMF and World Bank. The study uses the gravity model of bilateral trade flows to empirically investigate the effects of RTAs on intraregional trade on a set of 46 Sub-Saharan African (SSA) countries during 1995-2011 period. Our results indicate that three of the four selected RTAs have positive and statistically significant effect on the trade among the sub-Saharan African countries. Other included variables including distance, common language, shared border, shared colonial links, and common currency are found to be important determinants of trade among SSA countries.

The least squares (LS) estimator suffers from significant downward bias in autoregressive models that include an intercept. By construction, the LS estimator yields the best in-sample fit among a class of linear estimators notwithstanding its bias. Then, why do we need to correct for the bias? To answer this question, we evaluate the usefulness of the two popular bias correction methods, proposed by Hansen (1999) and So and Shin (1999), by comparing their out-of-sample forecast performances with that of the LS estimator. We find that bias-corrected estimators overall outperform the LS estimator. Especially, Hansen's grid bootstrap estimator combined with a rolling window method performs the best.