Structural changes in exports of an emerging economy: Case of Turkey

This paper indicates that Turkish exports are subject to structural changes as Turkey integrates into global production networks. Integration, which leads vertical specialization in production and changes in the commodity composition of Turkish exports in favor of non-traditional commodities, paces up during economic reform periods. As the export shares of non-traditional commodities, which have higher import and income sensitivity but lower real exchange rate elasticity, increases, coefficients of the aggregate export function change accordingly. Nevertheless, high import and income elasticity of exports imply that the global growth pattern plays a significant role in determining exports of Turkey.     

Cyclical Dynamics of the Turkish Economy and the Stock Market

Abstract

We analyze the cyclical dynamics of the Turkish economy and the stock market as well as their interactions. We use hidden Markov models that are robust to parameter instability arising from major shifts in economic policy, which have been typically observed in the Turkish economy. These models provide estimates of turning points for the growth, business, and stock market cycles. We identify three states of growth cycles and two states of business cycles in Turkey characterized by different mean estimates. We find that the economy went through five recessions since 1987. Crises are characterized by sharp drops in economic activity and are preceded by slowdowns. These crises are typically followed by strong recoveries during which the economy grows above its long-run average rate. We show that the Turkish stock market goes through three regimes having distinct risk-return dynamics. Bear markets associated with negative returns precede every recession with an average lead time of three quarters, suggesting that the stock market may be a useful forward-looking indicator of the Turkish economy.     

Foreign direct investment and business cycle co-movements: The panel data evidence

The previous literature has largely overlooked the possible channels through which foreign direct investment (FDI) might influence business cycle synchronization. In this study we analyze the linkages that exist among FDI, trade and industrial dissimilarity in relation to business cycle co-movements using a panel data set taken from 77 pairs of developed countries. The error component three-stage least squares (EC3SLS) estimates from a simultaneous equations model with panel data are shown to be superior to the estimates obtained from single equation models or simultaneous equations models with cross-sectional data. Our results indicate that FDI serves as a channel of international business cycle transmission that is equally important as the channels of trade and monetary policy. On the contrary, industrial dissimilarity is identified as having an indirect impact on the business cycle correlation through trade and FDI. Furthermore, our findings suggest that in our sample FDI is of the horizontal type and tends to substitute for trade.     

Determinants of foreign direct investment

Empirical studies of bilateral foreign direct investment (FDI) activity show substantial differences in specifications with little agreement on the set of included covariates. We use Bayesian statistical techniques that allow one to select from a large set of candidates those variables most likely to be determinants of FDI activity. The variables with consistently high inclusion probabilities include traditional gravity variables, cultural distance factors, relative labour endowments and trade agreements. There is little support for multilateral trade openness, most host‐country business costs, host‐country infrastructure and host‐country institutions. Our results suggest that many covariates found significant by previous studies are not robust.     

MONTHLY GDP FORECASTING USING BRIDGE MODELS: APPLICATION FOR THE FRENCH ECONOMY

This paper presents a model to predict French gross domestic product (GDP) quarterly growth rate. The model is designed to be used on a monthly basis by integrating monthly economic information through bridge models, for both supply and demand sides, allowing thus economic interpretations. For each GDP component, bridge equations are specified by using a general‐to‐specific approach implemented in an automated way by Hoover and Perez and improved by Krolzig and Hendry. This approach allows to select explanatory variables among a large data set of hard and soft data. A rolling forecast study is carried out to assess the forecasting performance in the prediction of aggregated GDP, by taking publication lags into account in order to run pseudo real‐time forecasts. It turns out that the model outperforms benchmark models. The results show that changing the set of equations over the quarter is superior to keeping the same equations over time. In addition, GDP growth seems to be more precisely predicted from a supply‐side approach rather than a demand‐side approach.     

What drives German foreign direct investment? New evidence using Bayesian statistical techniques

Despite the importance of Germany as an issuer of foreign direct investment (FDI), the studies analyzing its determinants are far from conclusive. This research contributes to filling this gap providing new evidence for the period 1996–2012. In order to reduce model uncertainty, we adopt a Bayesian model averaging (BMA) approach. We find that determinants associated with horizontal FDI appear to be dominant for explaining FDI in developed countries while for the group of developing countries covariates associated with vertical FDI motives play a larger role. Within Europe, while the majority of FDI is horizontally driven in “core” countries, in the “periphery” vertical motivations seem to prevail. Moreover, our results are compatible with more complex FDI models where vertical determinants and institutional variables are gaining prominence as does the leading role currently played by Germany in global value chains (GVC). Our results may provide hints for policymakers’ strategies to attract German investment.     

Out‐of‐Sample Exchange Rate Forecasting and Macroeconomic Fundamentals: The Case of Japan

This study explores the respective out‐of‐sample exchange rate forecasting abilities of five macroeconomic fundamental models in comparison to a naïve random walk model for Japan during the post‐Bretton Woods era. To assess the influence of major economic changes, we estimate both linear and nonlinear models for all the macroeconomic fundamentals. Overall, most structural exchange rate models outperform a naïve random walk model in terms of forecasting accuracy in the short horizon. When the fundamentals are only linearly modelled, the forecasting ability of the Taylor rule is generally superior to other fundamental models. When the fundamentals are nonlinearly specified, the predictability of some other models rises dramatically to match that of the Taylor rule models in short and/or long horizons. Of importance, we determine that the yen/dollar exchange rate forecasting performance effectively improves in several fundamental models when influential economic changes are incorporated.     

Instrumental variables and wavelet decompositions

The application of wavelet analysis provides an orthogonal decomposition of a time series by time scale, thereby facilitating the decomposition of a data series into the sum of a structural component and a random error component. The structural components revealed by the wavelet analysis yield nearly ideal instrumental variables for variables observed with error and for co-endogenous variables in simultaneous equation models. Wavelets also provide an efficient way to explore the path of the structural component of the series to be analyzed and can be used to detect some specification errors. The methodology described in this paper is applied to the errors in variables problem and simultaneous equations case using some simulation exercises and to the analysis of a version of the Phillips curve with interesting results.     

影响FDI在安徽分布的因素分析——基于区位选择模型

通过对已有FD I区域分布研究成果的梳理,选择11个指标作为FD I在安徽区域分布影响因素的解释变量,采用区位选择模型方法,进行逐步回归分析,最终剔除8个,认为影响安徽省FD I分布的因素主要有3个,即FD I的聚集效应、基础设施建设和各市社会消费品零售总额。     

Spatial effects of foreign direct investment in US states

Abstract This paper estimates the aggregate productivity effects of Marshallian externalities generated by foreign direct investment (FDI) in US states, controlling for Marshallian externalities and other spatial spillovers generated by domestic firms. A regional production function framework models externalities and other spatial spillovers explicitly as determinants of total factor productivity. We employ a system generalized method of moments (GMM) estimator to account for the potential endogeneity of FDI and the presence of spatial lags. Using data for US states from 1977–2003, the results indicate that FDI generates positive externalities, while externalities from domestic firms are negative.     

EFFECT OF OIL PRICES ON STOCK MARKETS: EVIDENCE FROM NEW GENERATION OF STAR MODEL

This paper aims to introduce an evidence of new generations of smooth transition regression model (STAR). It proposes two different forms of STAR model. First: a time varying STAR model (TVSTAR), which identify the estimated coefficients at each point of time. Second: a full specification STAR model (FSSTAR) which provides a consistent estimate even in the existence of some measurement errors, omitted variables and even if the true functional form is unknown. This study will consider the two proposed models and the traditional STAR model to examine the nonlinear relation between oil price and stock market index for two countries (Egypt and Turkey). Our results confirm the existing of a non‐linear relation between oil prices and stock return for both countries. The suggested models gives more accurate information about the time varying effect of oil price changes on stock markets and robust forecasts.     

Valuing cultural world heritage sites: an application of the travel cost method to Mont-Saint-Michel

This article, using the zonal version of the travel cost method (ZTCM), provides the first economic valuation of recreational trips to Mont-Saint-Michel (MSM). The MSM was designated as United Nations Educational, Scientific and Cultural Organization World Heritage Site in 1979, and is the most visited coastal site in France. The potential effects on consumer surplus (CS) estimates of some aspects of the ZTCM are considered, namely the treatment of the multiple purpose/destination (MP/D) trips, the inclusion of the opportunity cost of time (OCT) in the recreation demand model and the heterogeneity in the preferences. The heterogeneity in the preferences is examined through a random parameters Poisson model (RPPM). The RPPM generalizes the standard Poisson model (SPM) by allowing coefficients of explanatory variables to vary randomly across geographical units rather than being fixed. Our results show that (1) substantial CS values are associated with the MSM; (2) excluding/ignoring MP/D trips result in biased CS estimates; (3) omitting OCT does not substantially bias CS estimates; (4) the RPPM provides richer information about the impact of explanatory variables on the demand for trips than the SPM, but (5) leads to statistically similar CS estimates than the SPM.     

An analysis of Turkey's accession to the European Union

We build a two‐country dynamic general equilibrium model to study whether European citizens would benefit from the eventual accession of Turkey to the European Union (EU). The results of the simulations show that Turkey's accession is welfare enhancing for Europeans, provided that Turkish total factor productivity (TFP) increases sufficiently after enlargement. In the benchmark model with no capital mobility, the Europeans are better off if the Turkish TFP increase bridges more than 21% of the initial TFP gap between Turkey and the EU. This figure increases to 33% when capital mobility is introduced.     

The Beveridge curve and labour market dynamics in Turkey

This article investigates the dynamics of unemployment and vacancy rates in Turkey during the period 1951 to 2008 by means of a Beveridge Curve (BC). The time-series analysis of unemployment and vacancies as well as two other relevant labour market variables, real wages and real labour productivity, strongly suggests inefficiency in the Turkish labour market. A stable long-run relationship between unemployment rate and vacancy rate is found for Turkey, that is, the existence of a negatively sloped BC is verified. The estimated Turkish BC reflects the structural problems and lack of flexibility in the labour market. The modified BC with real wages and labour productivity reveals that labour productivity has no significant effect on unemployment rate whereas wages have positive and significant effects on the same variable.     

The export intensity of foreign affiliates in transition economiesThe importance of the organization of production1

Using a unique database containing trade and industry variables of foreign‐owned companies in the Polish manufacturing industry for the years 1993–2002, we investigate the relationship between the organizational structure of multinational enterprises (MNEs) in Poland and the export structure of their affiliates. We find that labour intensity and foreign control are the main explanatory variables for the export intensity of the affiliates. Given the overall rise in export intensity over the period of investigation, our findings suggest that export‐platform FDI has become a more important mode of organization for multinational enterprises as Poland has evolved into a relatively stable economic environment where MNEs feel comfortable basing their regional operations.     

Sectoral exports dynamics of Turkey: a panel co-integration analysis

This paper analyzes the role of unit labor costs and individual cost components in determining sectoral export dynamics and the change in the impact of these costs after the sequence of structural reforms in Turkey as of 2001. It employs a multivariate panel co-integration technique across 17 Turkish manufacturing sectors at different time periods. Results suggest that average elasticity changes not only between the time periods but also across the sectors. Cross-sectional elasticity differences are mostly attributable to the sectoral competitiveness and factor intensities.     

Nonlinear economic growth: Some theory and cross-country evidence

This paper aims to test the existence of different growth regimes, that is of different relationships between growth rate and income level. We propose a simple nonlinear growth model and test its empirical implications by estimating Markov transition matrices and stochastic kernels. We show that growth is indeed nonlinear: a first phase of slow or zero growth is followed by a take-off and, finally, by a phase of deceleration. We discuss the relevance of these results with respect to the issue of convergence and reversibility of development, in the light of models of structural change and technological diffusion.     

Inflation expectations in Turkey: learning to be rational

Turkey implemented an ambitious restructuring of the economy in the past several years, including the adoption of inflation targeting along with a floating exchange rate regime. Inflation came down from almost triple digits to single digits between 2001 and 2005. This particular episode of the Turkish economy sets a genuine case study for investigating the possible changes in the behaviour of inflation expectations upon a regime shift. Accordingly, this study analyses inflation expectations in Turkey, focusing especially on the post-2001 transition phase. We first conduct classical tests of unbiasedness and efficiency using aggregate survey data between August 2001 and October 2007 to get a statistical benchmark for rationality; we find that classical tests reject full rationality hypothesis for all series except next month's Consumer Price Index (CPI) inflation expectations. Then, we carry out Time-Varying Parameter (TVP) estimates based on a Kalman filter to see how the coefficients in the classical test equations evolve over time. This framework allows us to see whether there is convergence to rationality in terms of unbiasedness and efficiency. We find that forecast performance has improved through time, as the coefficients on the test equations shows movement towards values implied by unbiasedness and efficiency hypotheses, supporting the learning hypothesis.     

What drives bilateral foreign direct investment among Asian economies?

This study analyses the role of knowledge transfers via bilateral foreign direct investment (FDI) among 31 Asian economies. We make three distinct contributions to the literature on the drivers of FDI by: (1) applying the knowledge-capital model to FDI among Asian economies for the first time, using a comprehensive data set, and comparing it to an empirical gravity-type model of FDI; (2) conducting model selection tests to choose between alternative empirical specifications and estimation methods; and (3) modelling both the FDI participation decision and the decision on the amount of FDI. The main findings are: (1) while vertical FDI, driven by seeking low-cost unskilled labour, appears to be the dominant type of intra-Asian FDI, overall the knowledge-capital model is not supported by the data; and (2) conventional gravity variables (e.g., size, distance, common language) provide a better explanation of intra-Asian FDI and, therefore, a more suitable vehicle for future research.     

Structural analysis with mixed-frequency data: A model of US capital flows

We develop a new structural Vector Autoregressive (SVAR) model for analysis with mixed-frequency data. The MIDAS-SVAR model allows to identify structural dynamic links exploiting the information contained in variables sampled at different frequencies. It also provides a general framework to test homogeneous frequency-based representations versus mixed-frequency data models. A set of Monte Carlo experiments suggests that the test performs well both in terms of size and power. The MIDAS-SVAR is then used to study how monetary policy and financial uncertainty impact on the dynamics of gross capital inflows to the US. While no relation is found when using standard quarterly data, mixed frequency analysis exploiting the variability present in the series within the quarter shows that the effect of an interest rate shock is greater the longer the time lag between the month of the shock and the end of the quarter.