Panel time-series modeling: New tools for analyzing xt data
Stata already has an extensive range of built-in and user-written commands for analyzing xt (cross-sectional time-series) data. However, most of these commands do not take into account important features of the data relating to their time-series properties or cross-sectional dependence. This talk reviews the recent literature concerned with these features with reference to the types of data in which they arise. Most of the talk will be spent discussing and illustrating various Stata commands for analyzing these types of data, including several new user-written commands. The talk should be of general interest to users of xt data and of particular interest to researchers with panel datasets in which countries or regions are the unit of analysis and there is also a substantial time-series element. Over the past two decades, a literature dedicated to the analysis of macro panel data has concerned itself with some of the idiosyncrasies of this type of data, including variable nonstationarity and cointegration, as well as with the investigation of possible parameter heterogeneity across panel members and its implications for estimation and inference. Most recently, this literature has turned its attention to concerns over cross-sectional dependence, which can arise either in the form of unobservable global shocks that differ in their impact across countries (for example, the recent financial crisis) or as spillover effects (again, unobservable) between a subset of countries or regions.
References listed on IDEAS
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"Panel unit root tests in the presence of a multifactor error structure,"
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