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Econometric Analysis of Panel Data’ has become established as the leading textbook for postgraduate courses in panel data. This book is intended as a companion to the main text. The prerequisites include a good background in mathematical statistics and econometrics. The companion guide will add value to the existing textbooks on panel data by solving exercises in a logical and pedagogical manner, helping the reader understand, learn and teach panel data. These exercises are based upon those in Baltagi (2008) and are complementary to that text even though they are stand alone material and the reader can learn the basic material as they go through these exercises. The exercises in this book start by providing some background material on partitioned regressions and the Frisch-Waugh-Lovell theorem, showing the reader some applications of this material that are useful in practice. Then it goes through the basic material on fixed and random effects models in a one-way and two-way error components models, following the same outline as in Baltagi (2008). The book also provides some empirical illustrations and examples using Stata and EViews that the reader can replicate. The data sets are available on the Wiley web site (www.wileyeurope.com/college/baltagi).
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 2The 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 Grunfeld's data
4.10 Testing for random time and individual effects using Grunfeld's data
Section 4.3: Hausman’s test for correlated effects
4.11 The Hausman (1978) test based on a contrast of two estimators
4.12 The 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 The 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.
Chapter 5 : Heteroskedasticity and serial correlation
Section 5.1: Heteroskedastic error component model
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
Section 5.2: Serial correlation in the error component model
5.4 The AR(1) process
5.5 Unbiased estimates of the variance components under the AR(1) model
5.6 The AR(2) process
5.7 The AR(4) process for quarterly data
5.8 The MA(1) process
5.9 The 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
Chapter 6: Seemingly unrelated regressions with error components
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 components 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 components disturbances
Chapter 7: Simultaneous equations with error components
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 The equivalence of several EC2SLS estimators
7.6 Hausman test based on FE-2SLS vs. EC2SLS
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 The equivalence of several EC3SLS estimators
7.12 Special cases of the simultaneous equations model with one-way error components disturbances
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
Chapter 8: Dynamic panels
8.1 Bias of OLS, FE and RE estimators in a dynamic panel data model
8.2 The Anderson and Hsiao (1981) estimator
8.3 The Arellano and Bond (1991) estimator
8.4 Sargan's (1958) test of over-identifying 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 The Arellano and Bover (1995) estimator
8.10 Baltagi and Levin (1986): Dynamic demand for cigarettes
Chapter 9: Unbalanced panels
Section 9.1: The unbalanced one-way error component model
9.1 The 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 A comparison of variance components estimators using balanced versus unbalanced data
Section 9.2: The unbalanced two-way error component model
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
Section 9.3: Testing for individual and time effects using unbalanced panel data
9.10 The Breusch and Pagan (1980) LM test for unbalanced panel data
9.11 The local mean most powerful one-sided test for unbalanced panel data
9.12 The standardized Honda (1985) and King and Wu (1997) tests for unbalanced panel data
9.13 Harrison and Rubinfeld (1978): Hedonic Housing
Chapter 10: Special topics
Section 10.1: Measurement error and panel data
10.1 Measurement error and panel data
Section 10.2: Rotating panels
10.2 Rotating panel with two waves
10.3 Rotating panel with three waves
10.3 Spatial panels
10.4 The spatially autocorrelated error component model
10.5 Random effects and spatial autocorrelation with equal weights
10.4 Count panel data
10.6 Poisson panel regression model
10.7 Patents and R&D expenditures
Chapter 11: Limited dependent variables
11.1 The fixed effects logit model
11.2 The 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 The 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
Chapter 12: Nonstationary Panels
Section 12.1: Panel unit root tests
12.1 Panel unit root tests: GDP of G7 countries
Section 12.2: Panel cointegration tests
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