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Summary
Summary
This book is a companion to Baltagi's (2008) leading graduate econometrics textbook on panel data entitled Econometric Analysis of Panel Data, 4th Edition.
The book guides the student of panel data econometrics by solving exercises in a logical and pedagogical manner, helping the reader understand, learn and apply panel data methods. It is also a helpful tool for those who like to learn by solving exercises and running software to replicate empirical studies. It works as a complementary study guide to Baltagi (2008) and also as a stand alone book that builds up the reader's confidence in working out difficult exercises in panel data econometrics and applying these methods to empirical work.
The exercises start by providing some background information on partitioned regressions and the Frisch-Waugh-Lovell theorem. Then it goes through the basic material on fixed and random effects models in a one-way and two-way error components models: basic estimation, test of hypotheses and prediction. This include maximum likelihood estimation, testing for poolability of the data, testing for the significance of individual and time effects, as well as Hausman's test for correlated effects. It also provides extensions of panel data techniques to serial correlation, spatial correlation, heteroskedasticity, seemingly unrelated regressions, simultaneous equations, dynamic panel models, incomplete panels, measurement error, count panels, rotating panels, limited dependent variables, and non-stationary panels.
Author Notes
Badi H. Baltagi is Distinguished Professor of Economics, and Senior Research Associate at the Center for Policy Research, Syracuse University. He is a fellow of the Journal of Econometrics, a recipient of the Multa and Plura Scripsit Awards from Econometric Theory, and the Journal of Applied Econometrics Distinguished Authors Award.
Table of Contents
| Chapter 1 Partitioned regression and the Frisch-Waugh-Lovell theorem |
| 1.1 Partitioned regresion |
| 1.2 The Frisch-Waugh-Lovell theorem |
| 1.3 Residualing the constant |
| 1.4 Adding a dummy variable for the ith observation |
| 1.5 Computing forecasts and forecast standard errors |
| Chapter 2 The one-way error component model |
| Section 2.1 The one-way fixed effects model |
| 2.1 One-way fixed effects regression |
| 2.2 OLS and GLS for fixed effects |
| 2.3 Testing for fixed effects |
| Section 2.2 The one-way random effects model |
| 2.4 Variance-covariance matrix of the one-way random effects model |
| 2.5 The Fuller and Battese (1973) transformation for the one-way random effects model |
| 2.6 Unbiased estimates of the variance components: the one-way model |
| 2.7 Feasible unbiased estimates of the variance components: the one-way model |
| 2.8 Gasoline demand in the OECD |
| 2.9 System estimation of the one-way model: OLS versus GLS |
| 2.10 GLS is a matrix weighted average of Between and Within |
| 2.11 Efficiency of GLS compared to Within and Between estimators |
| 2.12 MLE of the random effects model |
| 2.13 Prediction in the one-way random effects model |
| 2.14 Mincer wage equation |
| 2.15 Bounds for s-& in a one-way random effects model |
| 2.16 Heteroskedastic fixed effects models |
| Chapter 3 The two-way error component model |
| Section 3.1 The two-way fixed effects model |
| 3.1 Two-way fixed effects regression |
| Section 3.2 The two-way random effects model |
| 3.2 Variance-covariance matrix of the two-way random effects model |
| 3.3 The Fuller and Battese (1973) transformation for the two-way random effects model |
| 3.4 Unbiased estimates of the variance components: the two-way model |
| 3.5 Feasible unbiased estimates of the variance components: the two-way model |
| 3.6 System estimation of the two-way model: OLS versus GLS |
| 3.7 Prediction in the two-way random effects model |
| 3.8 Variance component estimation under misspecification |
| 3.9 Bounds for s-& in a two-way random effects model |
| 3.10 Nested effects |
| 3.11 Three-way error component model |
| 3.12 A mixed-error component model |
| 3.13 Productivity of public capital in private production |
| Chapter 4 Test of hypotheses using panel data |
| Section 4.1 Tests for poolability of the data |
| 4.1 The Chow (1960) test |
| 4.2 The Roy (1957) and Zellner (1962) test |
| Section 4.2 Tests for individual and time effects |
| 4.3 The Breusch and Pagan (1980) Lagrange-multiplier test |
| 4.4 Local mean most powerful one-sided test |
| 4.5 The standardized Honda (1985) test |
| 4.6 The standardized King and Wu (1997) test |
| 4.7 Conditional Lagrange multiplier test: random individual effects |
| 4.8 Conditional Lagrange multiplier test: random time effects |
| 4.9 Testing for poolability using GrunfeldG+&s data |
| 4.10 Testing for random time and individual effects using GrunfeldG+&s data |
| Section 4.3 Hausman's test for correlated effects |
| 4.11 The Hausman (1978) test based on a contrast of two e |