In recent years many empirical studies of environmental Kuznets curves employing unit root and cointegration techniques have been conducted for both time series and panel data.When using such methods several issues arise: the effects of a short time dimension, in a panel context the effects of cross-sectional dependence, and the presence of nonlinear transformations of integrated variables. We discuss and illustrate how ignoring these problems and applying standard methods leads to questionable results. Using an estimation approach that addresses the second and third problem we find no evidence for an inverse Ushaped relationship between GDP and CO 2 emissions. KeywordsCarbon Kuznets Curve, panel data, unit roots, cointegration, crosssectional dependence, nonlinear transformations of regressors JEL Classification C12, C13, Q20 CommentsThe comments of Klaus Neusser, Georg Müller-Fürstenberger, and Reto Tanner are gratefully acknowledged. The usual disclaimer applies.
This paper develops a fully modified OLS estimator for cointegrating polynomial regressions, i.e. for regressions including deterministic variables, integrated processes and powers of integrated processes as explanatory variables and stationary errors. The errors are allowed to be serially correlated and the regressors are allowed to be endogenous. The paper thus extends the fully modified approach developed in Phillips and Hansen (1990). The FM-OLS estimator has a zero mean Gaussian mixture limiting distribution, which is the basis for standard asymptotic inference. In addition Wald and LM tests for specification as well as a KPSS-type test for cointegration are derived. The theoretical analysis is complemented by a simulation study which shows that the developed FM-OLS estimator and tests based upon it perform well in the sense that the performance advantages over OLS are by and large similar to the performance advantages of FM-OLS over OLS in cointegrating regressions.
This paper presents results on the size and power of first generation panel unit root and stationarity tests obtained from a large scale simulation study. The tests developed in the following papers are included: Levin et al. (2002), Harris and Tzavalis (1999), Breitung (2000), Im et al. (1997, 2003), Maddala and Wu (1999), Hadri (2000), and Hadri and Larsson (2005). Our simulation set-up is designed to address inter alia the following issues. First, we assess the performance as a function of the time and the cross-section dimensions. Second, we analyze the impact of serial correlation introduced by positive MA roots, known to have detrimental impact on time series unit root tests, on the performance. Third, we investigate the power of the panel unit root tests (and the size of the stationarity tests) for a variety of first order autoregressive coefficients. Fourth, we consider both of the two usual specifications of deterministic variables in the unit root literature.Panel stationarity test, Panel unit root test, Power, Simulation study, Size,
SummaryThis paper clarifies some conceptual shortcomings of the empirical environmental Kuznets curve (EKC) literature that arise because of the hitherto inadequate application of unit root and cointegration techniques. The literature to date has ignored the fact, and a fortiori the consequences, that powers of integrated processes are themselves not integrated processes. The paper explains why standard methods should not be applied and discusses some recently proposed viable estimation and testing approaches for cointegrating polynomial regressions. The application to CO2 and SO2 emissions data shows that using appropriate methods leads to strongly reduced evidence for a cointegrating EKC compared to typical but conceptually not sound findings. Copyright © 2014 John Wiley & Sons, Ltd.
Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractFocussing on the prime example of CO 2 emissions, we discuss several important theoretical and econometric problems that arise when studying environmental Kuznets curves (EKCs).The dominant theoretical approach is given by integrated assessment modelling, which consists of economic models that are combined with environmental impactmodels. We critically evaluate the aggregation, model dynamics and calibration aspects and their implications for the validity of the results. We then turn to a discussion of several important econometric problems that go almost unnoticed in the literature. The most fundamental problems relate to nonlinear transformations of nonstationary regressors and, in a nonstationary panel context, to neglected cross-sectional dependence. We discuss the implications of these two major and some minor problems that arise in the econometric analysis of Kuznets curves. Our discussion shows that EKC modelling as performed to date is subject to major drawbacks at both the theoretical and the econometric level. KeywordsCarbon Kuznets curve, integrated assessment models, regressions with integrated variables, nonstationary panels JEL Classification Q20, C12, C13 CommentsWe gratefully appreciate the helpful comments of two anonymous reviewers and the editor, which have led to substantial improvements of the paper. Furthermore, the comments of Gregor Bäurle, Robert Kunst, Klaus Neusser, and Reto Tanner are gratefully acknowledged. We thank Benito Müller for providing the data used in this study and for discussions on the topic. The usual disclaimer applies.
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