A Companion to Econometric Analysis of Panel Data
Author:
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Badi H. Baltagi |
| Publisher: |
Wiley |
| Copyright: |
2009 |
| ISBN-13: |
978-0-470-74403-1 |
| Pages: |
312; paperback |
| Price: |
$39.50 |
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Comment from the Stata technical group
A Companion to Econometric Analysis of Panel Data will appeal to
students and researchers learning about panel data with Baltagi’s
Econometric Analysis of Panel Data, 4th Edition
and other graduate-level econometrics texts. Companion
includes derivations of results shown without proof in Baltagi’s
textbook and provides more numerical examples, most of which were completed
using Stata.
Working through Companion is like having a personal tutor help you
work through a graduate text like Baltagi’s. The organization of
material in Companion parallels that of Baltagi’s panel-data
text but presents the material in a pedagogical manner consisting of some
background material followed by many sample exercise questions and complete
solutions. While one may at first frown at the idea of a book including
solutions to exercises, this text delivers them in a way that truly motivates
learning rather than mere copying: the questions are sufficiently involved
that by seeing how to solve them, students develop a method to attack
mathematical proofs and think like an econometrician.
Baltagi’s A Companion to Econometric Analysis of Panel Data,
while focused on panel data, is more generally useful to all students of
econometrics, because it teaches and instills tools that are applicable to an
array of problems.
Table of contents
Preface.
1 Partitioned Regression and the Frisch–Waugh–Lovell Theorem
Exercises.
1.1 Partitioned regression
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
2 The One-way Error Component Model
2.1 The One-way Fixed Effects Model
Exercises.
2.1 One-way fixed effects regression
2.2 OLS and GLS for fixed effects
2.3 Testing for fixed effects
2.2 The One-way Random Effects Model
Exercises.
2.4 Variance–covariance matrix of the one-way random effects model
2.5 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 Maximum likelihood estimation of the random effects model
2.13 Prediction in the one-way random effects model
2.14 Mincer wage equation
2.15 Bounds for s2 in a one-way random effects model
2.16 Heteroskedastic fixed effects models
3 The Two-way Error Component Model
3.1 The Two-way Fixed Effects Model
Exercise.
3.1 Two-way fixed effects regression
3.2 The Two-way Random Effects Model
Exercises.
3.2 Variance–covariance matrix of the two-way random effects model
3.3 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 s2, 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
4 Test of Hypotheses Using Panel Data
4.1 Tests for Poolability of the Data
Exercises.
4.1 Chow (1960) test
4.2 Roy (1957) and Zellner (1962) test
4.2 Tests for Individual and Time Effects
Exercises.
4.3 Breusch and Pagan (1980) Lagrange multiplier test
4.4 Locally mean most powerful one-sided test
4.5 Standardized Honda (1985) test
4.6 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 Grunfeld’s data
4.10 Testing for random time and individual effects using Grunfeld’s data
4.3 Hausman’s Test for Correlated Effects
Exercises.
4.11 Hausman (1978) test based on a contrast of two estimators
4.12 Hausman (1978) test based on an artificial regression
4.13 Three contrasts yield the same Hausman test
4.14 Testing for correlated effects in panels
4.15 Hausman’s test as a Gauss–Newton regression
4.16 Hausman’s test using Grunfeld’s data
4.17 Relative efficiency of the between estimator with respect to the within estimator
4.18 Hausman’s test using Munnell’s data
4.19 Currency Union and Trade
5 Heteroskedasticity and Serial Correlation
5.1 Heteroskedastic Error Component Model
Exercises.
5.1 Heteroskedastic individual effects
5.2 An alternative heteroskedastic error component model
5.3 An LM test for heteroskedasticity in a one-way error component model
5.2 Serial Correlation in the Error Component Model
Exercises.
5.4 AR(1) process
5.5 Unbiased estimates of the variance components under the AR(1) model
5.6 AR(2) process
5.7 AR(4) process for quarterly data
5.8 MA(1) process
5.9 MA(q) process
5.10 Prediction in the serially correlated error component model
5.11 A joint LM test for serial correlation and random individual effects
5.12 Conditional LM test for serial correlation assuming random individual effects
5.13 An LM test for first-order serial correlation in a fixed effects model
5.14 Gasoline demand example with first-order serial correlation
5.15 Public capital example with first-order serial correlation
6 Seemingly Unrelated Regressions with Error Components
Exercises.
6.1 Seemingly unrelated regressions with one-way error component disturbances
6.2 Unbiased estimates of the variance components of the one-way SUR model
6.3 Special cases of the SUR model with one-way error component disturbances
6.4 Seemingly unrelated regressions with two-way error component disturbances
6.5 Unbiased estimates of the variance components of the two-way SUR model
6.6 Special cases of the SUR model with two-way error component disturbances
7 Simultaneous Equations with Error Components.
7.1 Single Equation Estimation
Exercises.
7.1 2SLS as a GLS estimator
7.2 Within 2SLS and between 2SLS
7.3 Within 2SLS and between 2SLS as GLS estimators
7.4 Error component two-stage least squares
7.5 Equivalence of several EC2SLS estimators
7.6 Hausman test based on FE2SLS vs EC2SLS
7.2 System Estimation
Exercises.
7.7 3SLS as a GLS estimator
7.8 Within 3SLS and between 3SLS
7.9 Within 3SLS and between 3SLS as GLS estimators
7.10 Error component three-stage least squares
7.11 Equivalence of several EC3SLS estimators
7.12 Special cases of the simultaneous equations model with one-way
error component disturbances
7.3 Endogenous Effects
Exercises.
7.13 Mundlak’s (1978) augmented regression
7.14 Hausman and Taylor (1981) estimator
7.15 Cornwell and Rupert (1988): Hausman and Taylor application
7.16 Serlenga and Shin (2007): gravity models of intra-EU trade
7.17 Cornwell and Trumbull (1994): crime in North Carolina
8 Dynamic Panels
Exercises.
8.1 Bias of OLS, FE and RE estimators in a dynamic panel data model
8.2 Anderson and Hsiao (1981) estimator
8.3 Arellano and Bond (1991) estimator
8.4 Sargan’s (1958) test of overidentifying restrictions
8.5 Ahn and Schmidt (1995) moment conditions
8.6 Ahn and Schmidt (1995) additional moment conditions
8.7 Arellano and Bond (1991) weak instruments
8.8 Alternative transformations that wipe out the individual effects
8.9 Arellano and Bover (1995) estimator
8.10 Baltagi and Levin (1986): dynamic demand for cigarettes
9 Unbalanced Panels
9.1 The Unbalanced One-way Error Component Model
Exercises.
9.1 Variance–covariance matrix of unbalanced panels
9.2 Fixed effects for the one-way unbalanced panel data model
9.3 Wallace and Hussain (1969)-type estimators for the variance components
of a one-way unbalanced panel data model
9.4 Comparison of variance component estimators using balanced vs
unbalanced data
9.2 The Unbalanced Two-way Error Component Model
Exercises.
9.5 Fixed effects for the two-way unbalanced panel data model
9.6 Fixed effects for the three-way unbalanced panel data model
9.7 Random effects for the unbalanced two-way panel data model
9.8 Random effects for the unbalanced three-way panel data model
9.9 Wansbeek and Kapteyn (1989)-type estimators for the variance
components of a two-way unbalanced panel data model
9.3 Testing for Individual and Time Effects Using Unbalanced Panel Data
Exercises.
9.10 Breusch and Pagan (1980) LM test for unbalanced panel data
9.11 Locally mean most powerful one-sided test for unbalanced panel data
9.12 Standardized Honda (1985) and King and Wu (1997) tests for unbalanced panel data
9.13 Harrison and Rubinfeld (1978): hedonic housing
10 Special Topics
10.1 Measurement Error and Panel Data
Exercise.
10.1 Measurement error and panel data
10.2 Rotating Panels
Exercises.
10.2 Rotating panel with two waves
10.3 Rotating panel with three waves
10.3 Spatial Panels
Exercises.
10.4 Spatially autocorrelated error component model
10.5 Random effects and spatial autocorrelation with equal weights
10.4 Count Panel Data
Exercises.
10.6 Poisson panel regression model
10.7 Patents and R&D expenditures
11 Limited Dependent Variables
Exercises.
11.1 Fixed effects logit model
11.2 Equivalence of two estimators of the fixed effects logit model
11.3 Dynamic fixed effects logit model with no regressors
11.4 Dynamic fixed effects logit model with regressors
11.5 Binary response model regression
11.6 Random effects probit model
11.7 Identification in a dynamic binary choice panel data model
11.8 Union membership
11.9 Beer taxes and motor vehicle fatality rates
12 Nonstationary Panels
12.1 Panel Unit Root Tests
Exercise.
12.1 Panel unit root tests: GDP of G7 countries
12.2 Panel Cointegration Tests
Exercises.
12.2 Panel cointegration tests: manufacturing shipment and inventories
12.3 International R&D spillover
References
Index
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