Abdulnasser Hatemi-J

Is the tourism-led growth hypothesis valid for Turkey?

Like many developing countries, Turkey has also given priority to the development of tourism industry as a part of its economic growth strategy. This study intends to investigate whether tourism has really contributed to the economic growth in Turkey. The interaction between tourism and economic growth is investigated by making use of leveraged bootstrap causality tests. This method is robust to the existence of non-normality and ARCH effects. Special attention is given to the choice of the optimal lag order of the empirical model. It is found that the tourism-led growth hypothesis is supported empirically in the case of Turkey.

Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application

Causality tests in the Grangers sense are increasingly applied in empirical research. Since the unit root revolution in time-series analysis, several modifications of tests for causality have been introduced in the literature. One of the recent developments is the Toda–Yamamoto modified Wald (MWALD) test, which is attractive due to its simple application, its absence of pre-testing distortions, and its basis on a standard asymptotical distribution irrespective of the number of unit roots and the cointegrating properties of the data. This study investigates the size properties of the MWALD test and finds that in small sample sizes this test performs poorly on those properties when using its asymptotical distribution, the chi-square. It is suggested that use be made of a leveraged bootstrap distribution to lower the size distortions. Monte Carlo simulation results show that an MWALD test based on a bootstrap distribution has much smaller size distortions than corresponding cases when the asymptotic distribution is used. These results hold for different sample sizes, integration orders, and error term processes (homoscedastic or ARCH). This new method is applied to the testing of the efficient market hypothesis.

A new method to choose optimal lag order in stable and unstable VAR models

A crucial aspect of empirical research based on the vector autoregressive (VAR) model is the choice of the lag order, since all inference in the VAR model is based on the chosen lag order. Here, a new information criterion is introduced for this purpose. The conducted Monte Carlo simulation experiments show that this new information criterion performs well in picking the true lag order in stable as well as unstable VAR models.

On the Causality between Exchange Rates and Stock Prices: A Note

This study uses a new Granger non-causality testing procedure developed by Toda and Yamamoto (1995) to contribute to the debate on exchange rates and stock prices in Sweden. It examines a possible causal relation between these variables in a vector autoregression (VAR) model. The results show that Granger causality is unidirectional running from stock prices to effective exchange rates. The results also reveal that an increase in Swedish stock prices is associated with an appreciation of the Swedish krona. Special attention is given to the estimation methodology and the lag choosing process. Copyright 2002 by Blackwell Publishing Ltd and the Board of Trustees of the Bulletin of Economic Research

A test for multivariate ARCH effects

This paper extends Engles LM test for ARCH affects to multivariate cases. The size and power properties of this multivariate test for ARCH effects in VAR models are investigated based on asymptotic and bootstrap distributions. Using the asymptotic distribution, deviations of actual size from nominal size do not appear to be very excessive. Nevertheless, there is a tendency for the actual size to overreject the null hypothesis when the nominal size is 1% and underreject the null when the nominal size is 5% or 10%. It is found that using a bootstrap distribution for the multivariate LM test is generally superior in achieving the appropriate size to using the asymptotic distribution when (1) the nominal size is 5%; (2) the sample size is small (40 observations) and/or the VAR system is stable. With a small sample, the power of the test using the bootstrap distribution also appears better at the 5% nominal size.This paper extends Engles LM test for ARCH affects to multivariate cases. The size and power properties of this multivariate test for ARCH effects in VAR models are investigated based on asymptotic and bootstrap distributions. Using the asymptotic distribution, deviations of actual size from nominal size do not appear to be very excessive. Nevertheless, there is a tendency for the actual size to overreject the null hypothesis when the nominal size is 1% and underreject the null when the nominal size is 5% or 10%. It is found that using a bootstrap distribution for the multivariate LM test is generally superior in achieving the appropriate size to using the asymptotic distribution when (1) the nominal size is 5%; (2) the sample size is small (40 observations) and/or the VAR system is stable. With a small sample, the power of the test using the bootstrap distribution also appears better at the 5% nominal size.

Exchange rates and stock prices interaction during good and bad times: evidence from the ASEAN4 countries

Using bootstrap causality tests with leveraged adjustments, the link between exchange rates and stock prices in Malaysia, Indonesia, Philippines and Thailand is investigated for the periods immediately before and during the 1997 Asian crisis. Two variables are found to be significantly linked in the non-crisis period but not at all during the crisis period. The implications of this result in terms of hedging, market efficiency, market integration and policy intervention are explained in the paper.

Is the J-Curve Effect Observable for Small North European Economies?

The present study tests for the J-curve for five North European countries—Belgium, Denmark, The Netherlands, Norway, and Sweden—using generalized impulse response functions. The results provide empirical support for the J-curve. Each country has an impulse response function generated from a vector error-correction model that suggests that after a depreciation, there will be a dip in the export-import ratio within the first half-year after the depreciation. The long-run export-import ratio appears to be higher than the low point of this early dip in almost all cases. Also, in most cases, the export-import ratio appears in many periods after the depreciation to be converging from below to a higher long-run equilibrium.

Export performance and economic growth nexus in Japan: a bootstrap approach

Abstract This paper investigates the causal relationship between export growth and economic growth in Japan by performing augmented Granger-causality tests using the bootstrap simulation technique. The estimated results show that the Granger-causality is bi-directional for the period 1960–1999. The established bi-directional causality suggests that the expansion of exports is an integral part of the economic growth process in Japan.

Forecasting properties of a new method to determine optimal lag order in stable and unstable VAR models

This simulation study investigates the forecasting performance of a new information criterion suggested by Hatemi-J (2003) to pick the optimal lag length in the stable and unstable vector autregression (VAR) models. The conducted Monte Carlo experiments reveal that this information criterion is successful in selecting the optimal lag order in the VAR model when the main aim is to draw ex-ante (forecasting) inference regardless if the VAR model is stable or not. In addition, the simulations indicate that this information criterion is robust to autoregressive conditional heteroskedasticity effects.

Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH

The performance of different information criteria – namely Akaike, corrected Akaike (AICC), Schwarz–Bayesian (SBC), and Hannan–Quinn – is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens.