Panel Data Econometrics, 274

Theoretical Contributions and Empirical Applications

Edited By

  • Badi Baltagi, Syracuse University, Syracuse, NY, U.S.A.

Panel data econometrics has evolved rapidly over the last decade. Dynamic panel data estimation, non-linear panel data methods and the phenomenal growth in non-stationary panel data econometrics makes this an exciting area of research in econometrics. The 11th international conference on panel data held at Texas A&M University, College Station, Texas, June 2004, witnessed about 150 participants and 100 papers on panel data. This volume includes some of the papers presented at that conference and other solicited papers that made it through the refereeing process.
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Economists: Academics, Professionals and Students

Book information

  • Published: May 2006
  • Imprint: ELSEVIER
  • ISBN:
  • 5-Year Impact Factor:


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Preface (Badi H. Baltagi)


1. On the Estimation and Inference of Panel Cointegration Model with Cross-Sectional Dependence (J. Bai, C. Kao)
1.1 Introduction
1.2 The model
1.3 Assumptions
1.4 OLS
1.5 FM Estimator
1.6 Feasible FM
1.7 Hypothesis testing
1.8 Monte Carlo Simulations
1.9 Conclusion
Appendix A1.1

2. A Full Heteroscedastic One-way Error Components Model: Pseudo-maximum Likelihood Estimation and Specification Testing (B. Lejeune)
2.1 Introduction
2.2 The model
2.3 Pseudo-maximum likelihood estimation
2.3.1 The GPML2 estimator
2.3.2 Asymptotic properties of the GPML2 estimator
2.4 Specification testing
2.4.1 Conditional mean diagnostic tests
2.4.2 Conditional variance diagnostic tests
2.5 An empirical illustration
2.5.1 Data and model
2.5.2 Estimation and specification testing
2.6 Conclusion
Appendix A2.1
Appendix A2.2

3. Finite Sample Properties of FGLS Estimator for Random-effects Model under Non-normality (A. Ullah, X. Huang)
3.1 Introduction
3.2 Main results
3.3 Derivation
3.4 Numerical results
3.5 Conclusion
Appendix A3.1

4. Modelling the Initial Conditions in Dynamic Regression Models of Panel Data with Random Effects (I. Kazemi, R. Crouchley)
4.1 Introduction
4.2 The model with random effects
4.3 The likelihood and initial conditions
4.4 Maximum likelihood
4.5 The full likelihood
4.6 Modelling the initial conditions as endogenous
4.6.1 The stationary case
4.6.2 A pragmatic solution
4.7 Empirical Analysis
4.7.1 Dynamic growth panel data models
4.7.2 Model selection
4.8 Recommendations

5. Time Invariant Variables and Panel Data Models: A Generalised Frisch-Waugh Theorem and its Implications (J. Krishnakumar)
5.1 Introduction
5.2 The Generalised Frisch-Waugh theorem
5.3 The known case: Mundlak¿s Model
5.4 Some interesting features
5.5 Extension to the case with time invariant variables
5.5.1 Without correlated effects
5.5.2 With correlated effects
5.6 Concluding remarks
Appendix A5.1
Appendix A5.2
Appendix A5.3


6. An Intertemporal Model of Rational Criminal Choice (R.C. Sickles, J. Williams)
6.1 Introduction
6.2 The model
6.3 Data
6.3.1 The 1958 Philadelphia Birth Cohort Study
6.3.2 The sample
6.3.3 Measuring social capital Current social capital stock Initial value of social capital stock
6.4 Empirical model
6.4.1 The earnings equations Estimation methodology for the earnings equations Earnings equation results
6.4.2 The Euler Equations Estimation methodology for the Euler Equations Euler Equation results
6.5 Conclusion

7. Swedish Liquor Consumption: New Evidence on Taste Change (B.H. Baltagi, J.M. Griffin)
7.1 Introduction
7.2 Past trends and research findings
7.3 The data, model, and choice of panel data estimator
7.4 Empirical results
7.4.1 Basic habits persistence model with and without age composition
7.4.2 Tests for autonomous taste change
7.4.3 Forecast comparison of two competing types of taste change
7.4.4 Could autonomous technical change really be due to leakages?
7.5 Possible explanation for autonomous taste change
7.6 Conclusions

8. Import Demand Estimation with Country and Product Effects: Application of Multi-way Unbalanced Panel Data Models to Lebanese Imports (R. Boumahdi, J. Chaaban, A. Thomas)
8.1 Introduction
8.2 The flexible import model
8.3 The multi-way unbalanced error-component panel data model
8.3.1 The fixed effects model
8.3.2 The random effects model
8.3.3 Specification tests
8.4 The data
8.5 Estimation results
8.6 Conclusion

9. Can Random Coefficient Cobb-Douglas Production Functions be Aggregated to Similar Macro Functions? (E. Biørn, T. Skjerpen, K.R. Wangen)
9.1 Introduction
9.2 Model and output distribution
9.2.1 Basic assumptions
9.2.2 The conditional distribution of output
9.2.3 Exact marginal origo moments of output
9.2.4 Approximations to the marginal origo moments of output
9.3 An approximate aggregate production function in origo moments
9.3.1 A Cobb-Douglas production function in origo moments
9.3.2 Aggregation by analogy and aggregation biases in output and in input elasticities
9.4 Data, microeconometric model and micro estimation
9.5 Empirical results
9.5.1 Estimates of exact-formulae moments, approximations and their components
9.5.2 Aggregation biases in scale and input elasticities
9.6 Conclusion and extensions
Appendix A9.1 Proofs
A9.1.1 Proof of equation (11)
A9.1.2 Proof of equation (26)
Appendix A9.2 Details on estimation and data
A9.2.1 Details on the ML estimation
A9.2.2 Data

10. Conditional Heteroskedasticity and Cross-Sectional Dependence in Panel Data: An Empirical Study of Inflation Uncertainty in the G7 Countries (R. Cermeño, K.B. Grier)
10.1 Introduction
10.2 Model
10.3 Empirical strategy
10.3.1 Specifying the mean equation
10.3.2 Identifying conditional variance-covariance processes
10.4 Inflation uncertainty in the G7 countries
10.4.1 Conditional heteroskedasticity and cross-sectional dependence in G7 inflation
10.4.2 The interrelationship between average inflation and inflation uncertainty
10.5 Conclusion

11. The Dynamics of Exports and Productivity at the Plant Level: A Panel Data Error Correction Model (ECM) Approach (M. Yasar, C.H. Nelson, R.M. Rejesus)
11.1 Introduction
11.2 Conceptual framework
11.3 Empirical approach and the data
11.3.1 The error correction model
11.3.2 The system GMM estimation procedure
11.4 Results
11.5 Conclusions and policy implications
Appendix A11.1 Calculation of plant-level total factor productivity
Appendix A11.2 Plant performance of exporters and non-exporters: export premia

12. Learning about the Long-run Determinants of Real Exchange Rates for Developing Countries: A Panel Data Investigation (I. Drine, C. Rault)
12.1 Introduction
12.2 Determinants of the real equilibrium exchange rate
12.3 Empirical investigation of the long term real exchange rate determinants
12.3.1 The econometric relationship to be tested and the data set
12.3.2 Econometric results and their economic interpretation
12.4 Conclusion
Appendix A12.1 Panel unit-root test results for developing countries

13. Employee Turnover: Less is Not Necessarily More? (M.N. Harris, K.K. Tang, Y-P. Tseng)
13.1 Introduction
13.2 Theories of employee turnover and productivity
13.3 Data, empirical model and estimation method
13.3.1 Business longitudinal survey
13.3.2 The empirical model
13.4 Empirical results
13.4.1 Results of production function estimation
13.4.2 Employee turnover and productivity
13.5 Conclusions
Appendix A13.1 The working sample and variable definitions
Appendix A13.2 A simple model of optimal turnover rate and coordination

14. Dynamic Panel Models with Directors¿ and Officers¿ Liability Insurance Data (G. D. Kaltchev)
14.1 Introduction
14.2 Data and variables
14.3 Results
14.4 Conclusions

15. Assessment of the Relationship between Income Inequality and Economic Growth: A Panel Data Analysis of the 32 Federal Entities of Mexico, 1960-2002 (A. Ortega-Díaz)
15.1 Introduction
15.2 Model
15.3 Data sets and measurement
15.4 Estimation
15.5 Factors that might affect the coefficient of inequality
15.5.1 Data quality
15.5.2 Outliers
15.5.3 Periods coverage and method of estimation
15.5.4 Different definitions of inequality and literacy
15.6 Grouping and regional analysis
15.7 Analysis with different inequality measures
15.8 Conclusions and possible extensions