Econometrics of Panel Data: Methods and Applications

by Erik Biørn

Overview

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.

Product Details

ISBN-13:	9780198753445
Publisher:	Oxford University Press
Publication date:	12/27/2016
Pages:	418
Product dimensions:	7.70(w) x 9.60(h) x 1.10(d)

About the Author

Erik Biørn is Professor Emeritus at the University of Oslo. From 1986 to 2014 he taught econometrics at all levels at this university. Previously he was a researcher at Statistics Norway. His publications include several articles on empirical and theoretical topics in panel data analysis, and the book Taxation, Technology, and the User Cost of Capital (1989, Elsevier).

Table of Contents

1. Introduction2. Regression Analysis: Fixed Effects ModelsAppendix 2A. Properties of GLSAppendix 2B. Kronecker-product Operations: Examples3. Regression Analysis: Random Effects ModelsAppendix 3A. Two Theorems related to GLS Estimation4. Regression Analysis with Heterogeneous CoefficientsAppendix 4A. Matrix Inversion and Matrix Products: Useful ResultsAppendix 4B. A Reinterpretation of the GLS Estimator5. Regression Analysis with Uni-Dimensional Variables6. Latent Heterogeneity Correlated with RegressorsAppendix 6A. Reinterpretation: Block-Recursive SystemAppendix 6B. Proof of Consistency of the Two-Step Estimators7. Measurement ErrorsAppendix 7A. Asymptotics for Aggregate Estimators8. Dynamics ModelsAppendix 8A. Within Estimation of the AR Coefficient: AsymptoticsAppendix 8B. Autocovariances and Correlograms y"i"t and Δ y"i"t9. Analysis of Discrete ResponseAppendix 9A. The General Binomial Model: ML EstimationAppendix 9B. The Multinomial Logit Model: Conditional ML Estimation10. Unbalanced Panel DataAppendix 10A. Between-Estimation: ProofsAppendix 10B. GLS Estimation: ProofsAppendix 10C. Estimation of Variance Components: Details11. Panel Data with Systematic UnbalanceAppendix 11A. On truncated normal distributionsAppendix 11B. Partial Effects in Censoring Models12. Multi-Equation ModelsAppendix 12A. Estimating the Error Components Covariance MatricesAppendix 12B. Matrix Differentiation: Useful ResultsAppendix 12C. Estimator Covariance Matrices in Interdependent Model