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Panel Data Analysis Using GAUSS

Panel Data Analysis Using GAUSS. 4 Kuan-Pin Lin Portland State University. Panel Data Analysis Hypothesis Testing. Panel Data Model Specification Pool or Not To Pool Random Effects vs. Fixed Effects Heterscedasticity Time Serial Correlation Spatial Correlation.

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Panel Data Analysis Using GAUSS

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  1. Panel Data AnalysisUsing GAUSS 4 Kuan-Pin LinPortland State University

  2. Panel Data AnalysisHypothesis Testing • Panel Data Model Specification • Pool or Not To Pool • Random Effects vs. Fixed Effects • Heterscedasticity • Time Serial Correlation • Spatial Correlation

  3. Fixed Effects vs. Random Effects • Hypothesis Testing

  4. Random Effects vs. Fixed Effects • Fixed effects estimator is consistent under H0 and H1; Random effects estimator is efficient under H0, but it is inconsistent under H1. • Hausman Test Statistic

  5. Random Effects vs. Fixed Effects • Alternative Hausman Test(Mundlak Approach) • Estimate the random effects model with the group means of time variant regressors: • F Test that g = 0

  6. Hypothesis Testing • Fixed Effects Model • Random Effects Model

  7. Heteroscedasticity • The Null Hypothesis • Based on the auxiliary regression • LM test statistic is NR2 ~ 2(K), N is total number of observation (i,t)s.

  8. Cross Sectional Correlation • The Null Hypothesis • Based on the estimated correlation coefficients • Breusch-Pagan LM Test (Breusch, 1980) • As T  ∞ (N fixed)

  9. Cross Sectional Correlation • Bias adjusted Breusch-Pagan LM Test (Pesaran, et.al. 2008)

  10. Time Serial Correlation • The Model and Null Hypothesis • LM Test Statistic

  11. Joint Hypothesis TestingRandom Effects and Time Serial Correlation • The Model • Joint Test for AR(1) and Random Effects

  12. Joint Hypothesis TestingRandom Effects and Time Serial Correlation • Based on OLS residuals :

  13. Joint Hypothesis TestingRandom Effects and Time Serial Correlation • Marginal Tests for AR(1) & Random Effects • Robust Test for AR(1) & Random Effects • Joint Test Equivalence

  14. Panel Data AnalysisExtensions • Seeming Unrelated Regression • Allowing Cross-Equation Dependence • Fixed Coefficients Model • Dynamic Panel Data Analysis • Using FD Specification • IV and GMM Methods • Spatial Panel Data Analysis • Using Spatial Weights Matrix • Spatial Lag and Spatial Error Models

  15. References • Baltagi, B., Li, Q. (1995) Testing AR(1) against MA(1) disturbances in an error component model. Journal of Econometrics, 68, 133-151. • Baltagi, B., Bresson, G., Pirotte, A. (2006) Joint LM test for homoscedasticity in a one-way error component model. Journal of Econometrics, 134, 401-417. • Bera, A.K., W. Sosa-Escudero and M. Yoon (2001), Tests for the error component model in the presence of local misspecification, Journal of Econometrics 101, 1–23. • Breusch, T.S. and A.R. Pagan (1980), The Lagrange multiplier test and its applications to model specification in econometrics, Review of Economic Studies 47, 239–253. • Pesaran, M.H. (2004), General diagnostic tests for cross-section dependence in panels, Working Paper, Trinity College, Cambridge. • Pesaran, M.H., Ullah, A. and Yamagata, T. (2008), A bias-adjusted LM test of error cross-section independence, The Econometrics Journal,11, 105–127.

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