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Econometrics of Panel Data

Methods and Applications

Erik Biørn

October 2016

ISBN: 9780198753445

418 pages
Hardback
246x189mm

In Stock

Price: £75.00

A graduate text on panel data that takes the reader gradually from simple models and methods in scalar (simple vector) notation to more complex models in matrix notation.

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Description

A graduate text on panel data that takes the reader gradually from simple models and methods in scalar (simple vector) notation to more complex models in matrix notation.

  • Takes the reader from simple models and methods in scalar (simple vector) notation to more complex models in matrix notation
  • Covers 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 a natural progression
  • Suitable for master and advanced undergraduate courses

About the Author(s)

Erik Biørn, Professor Emeritus, University of Oslo

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:Introduction
    2:Regression Analysis: Fixed Effects Models
    Appendix 2A. Properties of GLS
    Appendix 2B. Kronecker-product Operations: Examples
    3:Regression Analysis: Random Effects Models
    Appendix 3A. Two Theorems related to GLS Estimation
    4:Regression Analysis with Heterogeneous Coefficients
    Appendix 4A. Matrix Inversion and Matrix Products: Useful Results
    Appendix 4B. A Reinterpretation of the GLS Estimator
    5:Regression Analysis with Uni-Dimensional Variables
    6:Latent Heterogeneity Correlated with Regressors
    Appendix 6A. Reinterpretation: Block-Recursive System
    Appendix 6B. Proof of Consistency of the Two-Step Estimators
    7:Measurement Errors
    Appendix 7A. Asymptotics for Aggregate Estimators
    8:Dynamics Models
    Appendix 8A. Within Estimation of the AR Coefficient: Asymptotics
    Appendix 8B. Autocovariances and Correlograms ᵧit and ᵧit
    9:Analysis of Discrete Response
    Appendix 9A. The General Binomial Model: ML Estimation
    Appendix 9B. The Multinomial Logit Model: Conditional ML Estimation
    10:Unbalanced Panel Data
    Appendix 10A. Between-Estimation: Proofs
    Appendix 10B. GLS Estimation: Proofs
    Appendix 10C. Estimation of Variance Components: Details
    11:Panel Data with Systematic Unbalance
    Appendix 11A. On truncated normal distributions
    Appendix 11B. Partial Effects in Censoring Models
    12:Multi-Equation Models
    Appendix 12A. Estimating the Error Components Covariance Matrices
    Appendix 12B. Matrix Differentiation: Useful Results
    Appendix 12C. Estimator Covariance Matrices in Interdependent Model