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Abstract:
We explain the proliferation of panel data studies in terms of (i) data availability, (ii) the more heightened capacity for modeling the complexity of human behavior than a single cross-section or time series data can possibly allow, and (iii) challenging methodology. Advantages and issues of panel data modeling are also discussed.

Panel data, Longitudinal data, Unobserved heterogeneity, Random effects, Fixed effects

Abstract:
This paper provides a review of linear panel data models with slope heterogeneity, introduces various types of random coefficients models and suggest a common framework for dealing with them. It considers the fundamental issues of statistical inference of a random coefficients formulation using both the sampling and Bayesian approaches. The paper also provides a review of heterogeneous dynamic panels, testing for homogeneity under weak exogeneity, simultaneous equation random coefficient models, and the more recent developments in the area of cross-sectional dependence in panel data models.

Random coefficient models, dynamic heterogeneous panels, classical and Bayesian approaches, tests of slope heterogeneity, cross section dependence

Abstract:
This paper considers estimation and inference in panel vector autoregressions (PVARs) with fixed effects when the time dimension of the panel is finite, and the cross-sectional dimension is large. A Maximum Likelihood (ML) estimator based on a transformed likelihood function is proposed and shown to be consistent and asymptotically normally distributed irrespective of the unit root and cointegrating properties of the underlying PVAR model. The transformed likelihood framework is also used to derive unit root and cointegration tests in panels with short time dimension; these tests have the attractive feature that they are based on standard chi-square and normal distributed statistics. Examining Generalized Method of Moments (GMM) estimation as an alternative to our proposed ML estimator, it is shown that conventional GMM estimators based on standard orthogonality conditons break down if the underlying time series contain unit roots. Also, the implementation of extended GMM estimators making use of variants of homoskedasticity and stationarity restrictions as suggested in the literature in a univariate context is subject to difficulties. Monte Carlo evidence is adduced suggesting that the ML estimator and parameter hypothesis and cointegration tests based on it perform well in small sample; this is in marked contrast to the small sample performance of the GMM estimators.

Panel vector autoregressions, fixed effects, unit roots, cointegration

Abstract:
We explain the proliferation of panel data studies in terms of (i) data availability, (ii) the more heightened capacity for modeling the complexity of human behavior than a single cross-section or time series data can possibly allow, and (iii) challenging methodology. Advantages and issues of panel data modeling are also discussed.

Panel data; Longitudinal data; Unobserved heterogeneity; Random effects; Fixed effects

Abstract:
This study examines the relationship between expected stock returns and volatility in the twelve largest international stock markets during January 1980 - December 2001. Consistent with most previous studies, we find a positive but insignificant relationship during the sample period for the majority of the markets based on parametric EGARCH-M models. However, using a flexible semiparametric specification of conditional variance, we find evidence of a significant negative relationship between expected returns and volatility in six out of the twelve markets under study. The results lend support to the recent claim (Bekaert and Wu, 2000; Whitelaw, 2000) that stock market returns are negatively correlated with stock market volatility.

risk-return tradeoff, GARCH, semiparametric estimation

Abstract:
This study examines the long-run price relationship and the dynamic price transmission among the U.S., Germany, and four major Eastern European emerging stock markets, with particular attention to the impact of the 1998 Russian financial crisis. The results show that both the long-run price relationship and the dynamic price transmission were strengthened among these markets after the crisis. The influence of Germany became noticeable on all the Eastern European markets only after the crisis but not before the crisis. We also conduct a rolling generalized VAR analysis to confirm the robustness of the main findings.

market linkages, emerging stock markets, generalized impulse response analysis, generalized forecast error variance decomposition, rolling VAR analysis

Abstract:
This study examines the long-run price relationship and the dynamic price transmission among the U.S., Germany, and four major Eastern European emerging stock markets, with particular attention to the impact of the 1998 Russian financial crisis. The results show that both the long-run price relationship and the dynamic price transmission were strengthened among these markets after the crisis. The influence of Germany became noticeable on all the Eastern European markets only after the crisis but not before the crisis. We also conduct a rolling generalized VAR analysis to confirm the robustness of the main findings.

Market linkages, emerging stock markets, generalized impulse response analysis, generalized forecast error variance decomposition, rolling VAR analysis

Abstract:
In this paper we discuss tests for residual cross section dependence in nonlinear panel data models. The tests are based on average pair-wise residual correlation coefficients. In nonlinear models, the definition of the residual is ambiguous and we consider two approaches: deviations of the observed dependent variable from its expected value and generalized residuals. We show the asymptotic consistency of the cross section dependence (CD) test of Pesaran (2004). In Monte Carlo experiments it emerges that the CD test has the correct size for any combination of N and T whereas the LM test relies on T large relative to N. We then analyze the roll-call votes of the 104th U.S. Congress and find considerable dependence between the votes of the members of Congress.

cross-section dependence, nonlinear panel data model

Abstract:
We consider a lag-augmented two- or three-stage least-squares estimator for a structural dynamic model of non-stationary and possibly cointegrated variables without the prior knowledge of unit roots or rank of cointegration. We show that the conventional two-and three-stage least-squares estimators are consistent but contain non-standard distributions without the strict exogeneity assumption; hence the conventional Wald type test statistics may not be chi-square distributed. We propose a lag order augmented two- or three-stage least-squares estimator that is consistent and asymptotically normally distributed. Limited Monte Carlo studies are conducted to shed light on the finite sample properties of various estimators.

Abstract:
We consider a lag-augmented two- or three-stage least squares estimator for a structural dynamic model of nonstationary and possibly cointegrated variables without the prior knowledge of unit roots or rank of cointegration. We show that the conventional two- and three-stage least squares estimators are consistent but contain nonstandard distributions without the strict exogeneity assumption, hence the conventional Wald type test statistics may not be chi-square distributed. We propose a lag order augmented two- or three-stage least squares estimator that is consistent and asymptotically normally distributed. Limited Monte Carlo studies are conducted to shed light on the finite sample properties of various estimators.

Structural vector autoregressions, Nonstationary time series, Cointegration, Hypothesis testing, Two and Three Stage Least Squares

Abstract:
We consider the estimation of a structural vector autoregressive model of nonstationary and possibly cointegrated variables without the prior knowledge of unit roots or rank of cointegration. We propose two modified two stage least squares estimators that are consistent and have limiting distributions that are either normal or mixed normal. Limited Monte Carlo studies are also conducted to evaluate their finite sample properties.

Structural vector autoregression, Unit root, Cointegration, Asymptotic properties, Hypothesis testing

Abstract:
We use Japanese aggregate and disaggregate money demand data to show that conflicting inferences can arise. The aggregate data appears to support the contention that there was no stable money demand function. The disaggregate data shows that there was a stable money demand function. Neither was there any indication of the presence of a liquidity trap. Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and disaggregate analysis are attributed to the neglected heterogeneity among micro units. We provide necessary and sufficient conditions for the existence of cointegrating relations among aggregate variables when heterogeneous cointegration relations among micro units exist. We also conduct simulation analysis to show that when such conditions are violated, it is possible to observe stable micro relations, but unit root phenomenon among macro variables. Moreover, the prediction of aggregate outcomes, using aggregate data is less accurate than the prediction based on micro equations and policy evaluation based on aggregate data ignoring heterogeneity in micro units can be grossly misleading.

Abstract:
Recent research convincingly shows that crises beget reform. Although the consensus is that economic crises foster macroeconomic stabilization, it is silent on which types of crises cause which types of reform. Is it economic or political crises that are the most important drivers of structural reforms? To answer this question we put forward evidence on trade and labour market liberalization from panel data on more than 100 developed and developing countries from 1950 to 2000. We find important differences in the effects of the two types of crises on the two reforms across regions and even from one measure of crisis to another. Yet, in general, we consistently find that political considerations (political crises as well as political institutions) are more important determinants of these reforms than economic crises. This finding is robust to the inclusion of interdependencies between the two types of crises, feedbacks between the two types of reform, the use of alternative measures of political and economic crises and whether or not the data are pooled across all countries or only across regions.

Economic reform, political crisis, labour market reform, economic crisis, trade liberalisation

Abstract:
Recent research convincingly shows that crises beget reform. Although the consensus is that economic crises foster macroeconomic stabilization, it is silent on which types of crises cause which types of reform. Is it economic or political crises that are the most important drivers of structural reforms? To answer this question we put forward evidence on trade and labour market liberalization from panel data on more than 100 developed and developing countries from 1950 to 2000. We find important differences in the effects of the two types of crises on the two reforms across regions and even from one measure of crisis to another. Yet, in general, we consistently find that political considerations (political crises as well as political institutions) are more important determinants of these reforms than economic crises. This finding is robust to the inclusion of interdependencies between the two types of crises, feedbacks between the two types of reform, the use of alternative measures of political and economic crises and whether or not the data are pooled across all countries or only across regions.

economic reform, economic crisis, political crisis, trade liberalisation, labour

Abstract:
In this paper we propose a nonparametric kernel-based model specification test that can be used when the regression model contains both discrete and continuous regressors. We employ discrete variable kernel functions and we smooth both the discrete and continuous regressors using least squares cross-validation methods. The test statistic is shown to have an asymptotic normal null distribution. We also prove the validity of using the wild bootstrap method to approximate the null distribution of the test statistic, the bootstrap being our preferred method for obtaining the null distribution in practice. Simulations show that the proposed test has significant power advantages over conventional kernel tests which rely upon frequency-based nonparametric estimators that require sample splitting to handle the presence of discrete regressors.

Consistent test, parametric functional form, nonparametric estimation

Abstract:
This article proposes a simulation approach to obtain least-squares or generalized least-squares estimators of structural nonlinear errors-in-variables models. The proposed estimators are computationally attractive because they do not need numerical integration nor huge numbers of simulations per observable. In addition, the asymptotic covariance matrix of the estimator has a simple decomposition that may be used to guide selection of appropriate simulation sizes. The method is also useful for models with missing data or imperfect surrogate covariates, where application of conventional least-squares and maximum-likelihood methods is restricted by numerical multidimensional integrations.

Abstract:
This paper deals with censored or truncated regression models where the explanatory variables are measured with additive errors. We propose a two-stage estimation procedure that combines the instrumental variable method and the minimum distance estimation. This approach produces consistent and asymptotically normally distributed estimators for model parameters. When the predictor and instrumental variables are normally distributed, we also propose a maximum likelihood based estimator and a two-stage moment estimator. Simulation studies show that all proposed estimators perform satisfactorily for relatively small samples and relatively high degree of censoring. In addition, the maximum likelihood based estimators are fairly robust against non-normal and/or heteroskedastic random errors in our simulations. The method can be generalized to panel data models.

Abstract:
The paper seeks to determine whether high interest rates have had the effect of appreciating nominal exchange rates in three Asian countries. The authors use high-frequency data for Korea, Malaysia, and Thailand during the recent crisis and its aftermath to examine the relationship between the increase in interest rates and the behavior of exchange rates. It is found that raising interest rates has had a small impact on nominal exchange rates during the crisis period.

Abstract:
In advanced industrial economies it is accepted that efficiency requires aligning managerial autonomy in decision-making with managerial incentives. Should this hold for economies like that of rural China where (at least until very recently) managers might abuse autonomy and government owners may have objectives other than profit maximization? This paper tests for the effects of managerial autonomy on efficiency with and without alignment with incentives in a panel of Chinese town and village enterprises (TVEs). The results show that managerial autonomy has had a positive and significant effect on productivity only when aligned with incentives.

Abstract:
In this paper we intend to survey and suggest the theoretical framework of the important aspects of causality detection with the purpose of conveying to the reader the essential features and the different forms in which inferences may be drawn from given data. Section II presents the basic theorem characterizing the causality events and suggests two feedback detection methods which, like the one suggested by Pierce and Haugh (1977), are based on correlation analysis. In Section III we survey other well-known causality detection methods and try to relate and to compare them with the methods suggested in Section II. Section IV briefly reviews the theoretical controversy of the relationship between money and income and presents some empirical evidence based on the methods discussed in this paper. Conclusions are in Section V.

Abstract:
This paper is concerned with the estimation of the parameters in a dynamic simultaneous equation model with stationary disturbances under the assumption that the variables are subject to random measurement errors. The conditions under which the parameters are identified are stated. An asymptotically efficient frequency-domain class of instrumental variables estimators is suggested. The procedure consists of two basic steps. The first step transforms the model in such a way that the observed exogenous variables are asymptotically orthogonal to the residual terms. The second step involves an iterative procedure like that of Robinson [13].

Abstract:
We explore the usefulness of combining opinion surveys with time-series data to forecast Japanese economy. We demonstrate that the businessmen's judgemental variables might summarize contemporaneous information beyond that of actual series. The combined models do yield substantially more accurate one-period or multi-period-ahead forecasts and can predict the turning points better than pure time-series models.

Abstract:
This paper uses a national survey of 200 Chinese town and village enterprises (TVEs) from 1985 to 1990 to explore empirically the effect of contractual arrangements on the performance of enterprises under Chinese institutional conditions. A theoretical model that emphasizes a potentially important role for local government effort in a situation of double-sided moral hazard is developed to explain why the share profit system may produce better incentives than the quota profit system. The paper shows that, from a profit maximazation perspective, the trend away from share profit contracts to quota profit contracts among Chineses TVEs in the late 1980s may have been premature.