Econometrics of panel data: methods and applications/ Erik Biorn

Includes bibliographical references and index

List of Tables -- 1. Introduction -- 2. Regrssion Analysis: Fixed effects models -- 3. Regression Analysis: Random Effects Models -- 4. Regression Analysis with Heterogeneous Coefficients -- 5. Regression Analysis with Unidimensional Variables -- 6. Latent Heterogeneity Correlated with Regressors -- 7. Measurement Errors -- 8. Dynamic Models -- 9. Analysis of Discrete Response -- 10. Unbalanced Panel Data -- 11. Panel data with Systematic Unbalance -- 12. Multi-Equation Models -- References -- Index

Panel data is a data type increasingly used in research in economics, social sciences, and medicine. Its primary characteristic is that the data variation goes jointly over space (across individuals, firms, countries, etc.) and time (over years, months, etc.). Panel data allow examination of problems that cannot be handled by cross-section data or time-series data. Panel data analysis is a core field in modern econometrics and multivariate statistics, and studies based on such data occupy a growing part of the field in many other disciplines. The book is intended as a text for master and advanced undergraduate courses. It may also be useful for PhD-students writing theses in empirical and applied economics and readers conducting empirical work on their own. The book attempts to take the reader gradually from simple models and methods in scalar (simple vector) notation to more complex models in matrix notation. A distinctive feature is that more attention is given to unbalanced panel data, the measurement error problem, random coefficient approaches, the interface between panel data and aggregation, and the interface between unbalanced panels and truncated and censored data sets. The 12 chapters are intended to be largely self-contained, although there is also natural progression. Most of the chapters contain commented examples based on genuine data, mainly taken from panel data applications to economics. Although the book, inter alia, through its use of examples, is aimed primarily at students of economics and econometrics, it may also be useful for readers in social sciences, psychology, and medicine, provided they have a sufficient background in statistics, notably basic regression analysis and elementary linear algebra.