JOURNAL OF APPLIED ECONOMETRICS J. Appl. Econ. 22: 229–232 (2007) Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/jae.955 HETEROGENEITY AND CROSS SECTION DEPENDENCE IN PANEL DATA MODELS: THEORY AND APPLICATIONS INTRODUCTION BADI H. BALTAGI a AND M. HASHEM PESARAN b a Department of Economics and Center for Policy Research, Syracuse University, USA b Faculty of Economics and CIMF, Cambridge University, UK, and USC SUMMARY The papers included in this special issue are primarily concerned with the problem of cross section dependence and heterogeneity in the analysis of panel data models and their relevance in applied econometric research. Cross section dependence can arise due to spatial or spill over effects, or could be due to unobserved (or unobservable) common factors. Much of the recent research on non-stationary panel data have focussed on this problem. It was clear that the first generation panel unit root and cointegration tests developed in the 1990’s, which assumed cross-sectional independence, are inadequate and could lead to significant size distortions in the presence of neglected cross-section dependence. Second generation panel unit root and cointegration tests that take account of possible cross-section dependence in the data have been developed, see the recent surveys by Choi (2006) and Breitung and Pesaran (2007). The papers by Baltagi, Bresson and Pirotte, Choi and Chue, Kapetanios, and Pesaran in this special issue are further contributions to this literature. The papers by Fachin, and Moon and Perron are empirical studies in this area. Controlling for heterogeneity has also been an important concern for empirical researchers with panel data methods promising better handle on heterogeneity than cross-section data methods. The papers by Hsiao, Shen, Wang and Weeks, Pedroni and Serlenga and Shin are empirical contributions to this area. Copyright  2007 John Wiley & Sons, Ltd. 1. THEORETICAL CONTRIBUTIONS Choi and Chue study subsampling hypothesis tests for panel data that may be nonstationary, cross-sectionally correlated, and cross-sectionally cointegrated. The subsampling approach to hypothesis testing allows the regressors to be stationary or nonstationary with unit roots, or they may be a mixture of both types. It also allows for cross-sectional correlation that need not be estimated. This implies that there is less chance of size distortions due to misspecification, say from procedures assuming factor structures. Cross-sectional cointegration is also allowed without requiring knowledge of the cointegration coefficients and ranks. The subsampling approach provides approximations to the finite sample distributions of the tests without estimating nuisance parameters. The tests include panel unit root and cointegration tests as special cases. The number of cross-sectional units is assumed to be finite and that of time-series observations infinite. It is shown that subsampling provides asymptotic distributions that are equivalent to the asymptotic distributions of the panel tests. In addition, the tests using critical values from subsampling are shown to be consistent. A number of panel unit root tests that allow for cross section dependence have been proposed in the literature that use orthogonalization type procedures to asymptotically eliminate the cross dependence of the series before standard panel unit root tests are applied to the transformed series. Copyright  2007 John Wiley & Sons, Ltd.