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Modified JJ

This chapter explores panel data models, including long and narrow, short and wide, and long and wide datasets. It also covers Grunfeld's Investment Data and sets of regression equations, as well as seemingly unrelated regressions, fixed effects model, and random effects model.

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Modified JJ

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  1. Panel Data Models Modified JJ Vera Tabakova, East Carolina University

  2. Chapter 15: Panel Data Models • Grunfeld’s Investment Data • Sets of Regression Equations • Seemingly Unrelated Regressions • The Fixed Effects Model • The Random Effects Model Principles of Econometrics, 3rd Edition

  3. Chapter 15: Panel Data Models The different types of panel data sets can be described as: • “long and narrow,” with “long” describing the time dimension and “narrow” implying a relatively small number of cross sectional units; • “short and wide,” indicating that there are many individuals observed over a relatively short period of time; • “long and wide,” indicating that both N and T are relatively large. Principles of Econometrics, 3rd Edition

  4. Grunfeld’s Investment Data The data consist of T = 20 years of data (1935-1954) for N = 10 large firms. Let yit = INVit and x2it = Vit and x3it = Kit Principles of Econometrics, 3rd Edition

  5. 2 Firms: all coefficient equal Principles of Econometrics, 3rd Edition

  6. 2 Firms: each has different coeffs Principles of Econometrics, 3rd Edition

  7. Sets of Regression Equations Assumption (5) says that the errors in both investment functions (i) have zero mean, (ii) are homoskedastic with constant variance, and (iii) are not correlated over time; autocorrelation does not exist. The two equations do have different error variances Principles of Econometrics, 3rd Edition

  8. Separate OLS estimation of 2 Firms Principles of Econometrics, 3rd Edition

  9. Seemingly Unrelated Regressions This assumption says that the error terms in the two equations, at the same point in time, are correlated. This kind of correlation is called a contemporaneous correlation. Principles of Econometrics, 3rd Edition

  10. Seemingly Unrelated Regressions GLS Econometric software includes commands for SUR (or SURE) that carry out the following steps: • Estimate the equations separately using least squares; • Use the least squares residuals from step (i) to estimate ; • Use the estimates from step (ii) to estimate the two equations jointly within a generalized least squares framework. GLS Principles of Econometrics, 3rd Edition

  11. GLS estimation of SUR Principles of Econometrics, 3rd Edition

  12. Separate OLS or Joint GLS Estimation? There are two situations where separate least squares estimation is just as good as the SUR technique : • when the equation errors are not contemporaneously correlated; • when the same explanatory variables appear in each equation. If the explanatory variables in each equation are different, then a test to see if the correlation between the errors is significantly different from zero is of interest. Principles of Econometrics, 3rd Edition

  13. Separate OLS or Joint GLS Estimation? # of regressors: Principles of Econometrics, 3rd Edition

  14. Separate OLS or Joint GLS Estimation? Testing for correlated errors for two equations: LM = 10.628 > 3.84 Hence we reject the null hypothesis of no correlation between the errors and conclude that there are potential efficiency gains from estimating the two investment equations jointly using GLS Principles of Econometrics, 3rd Edition

  15. Separate OLS or Joint GLS Estimation? If there are 3 instead of 2 firms Testing for correlated errors: Principles of Econometrics, 3rd Edition

  16. Separate OLS or Joint SUR Estimation? If there are M firms: Testing for correlated errors in M equations: Under the null hypothesis that there are no contemporaneous correlations, this LM statistic has a χ2-distribution with M(M–1)/2 degrees of freedom, in large samples. Principles of Econometrics, 3rd Edition

  17. Testing for Equality of Coefficients Most econometric software will perform an F-test and/or a Wald χ2–test; in the context of SUR equations both tests are large sample approximate tests. The F-statistic has J numerator degrees of freedom and (MTK) denominator degrees of freedom, where J is the number of hypotheses, M is the number of equations, and K is the total number of coefficients in the whole system, and T is the number of time series observations per equation. The χ2-statistic has J degrees of freedom. Principles of Econometrics, 3rd Edition

  18. The Fixed Effects Model We cannot consistently estimate the 3×N×T parameters in (9) with only NT total observations. Principles of Econometrics, 3rd Edition

  19. Dummies to capture Fixed Effect All behavioral differences between individual firms and over time are captured by the intercept. Individual intercepts are the only coeffs allowed to vary and “control” for these firm specific differences. All other coefficients are assumed equal. Principles of Econometrics, 3rd Edition

  20. Dummies for fixed effect This specification is sometimes called the least squares dummy variable model, or the fixed effects model. Principles of Econometrics, 3rd Edition

  21. A Dummy Variable Model Principles of Econometrics, 3rd Edition

  22. Test of equality of intercepts These N–1= 9 joint null hypotheses are tested using the usual F-test statistic. In the restricted model all the intercept parameters are equal. If we call their common value β1, then the restricted model is: Principles of Econometrics, 3rd Edition

  23. Pooled OLS regression • Here all coefficients are assumed equal. This model is a pooled regression used as the restricted model for the test. It disregards the heteroscedasticity. Principles of Econometrics, 3rd Edition

  24. Test of equality of coefficients We reject the null hypothesis that the intercept parameters for all firms are equal. We conclude that there are differences in firm intercepts, and that the data should not be pooled into a single model with a common intercept parameter. Principles of Econometrics, 3rd Edition

  25. The Fixed Effects Estimator Principles of Econometrics, 3rd Edition

  26. The Fixed Effects Estimator Principles of Econometrics, 3rd Edition

  27. The Fixed Effects Estimator Principles of Econometrics, 3rd Edition

  28. The Fixed Effects Estimator Principles of Econometrics, 3rd Edition

  29. The Fixed Effects Estimator Principles of Econometrics, 3rd Edition

  30. The Fixed Effects Estimator Principles of Econometrics, 3rd Edition

  31. The Random Effects Model Principles of Econometrics, 3rd Edition

  32. The Random Effects Model Because the random effects regression error in (24) has two components, one for the individual and one for the regression, the random effects model is often called an error components model. Principles of Econometrics, 3rd Edition

  33. Error Term Assumptions Principles of Econometrics, 3rd Edition

  34. Error Term Assumptions There are several correlations that can be considered. • The correlation between two individuals, i and j, at the same point in time, t. The covariance for this case is given by Principles of Econometrics, 3rd Edition

  35. Error Term Assumptions • The correlation between errors on the same individual (i) at different points in time, t and s. The covariance for this case is given by Principles of Econometrics, 3rd Edition

  36. Error Term Assumptions • The correlation between errors for different individuals in different time periods. The covariance for this case is Principles of Econometrics, 3rd Edition

  37. Error Term Assumptions Principles of Econometrics, 3rd Edition

  38. Testing for Random Effects Principles of Econometrics, 3rd Edition

  39. Estimation of the Random Effects Model Principles of Econometrics, 3rd Edition

  40. An Example Using the NLS Data Principles of Econometrics, 3rd Edition

  41. Endogeneity in the Random Effects Model If the random error is correlated with any of the right-hand side explanatory variables in a random effects model then the least squares and GLS estimators of the parameters are biased and inconsistent. Principles of Econometrics, 3rd Edition

  42. Chapter 15 Appendix • Appendix 15A Estimation of Error Components Principles of Econometrics, 3rd Edition

  43. Appendix 15A Estimation of Error Components Principles of Econometrics, 3rd Edition

  44. Appendix 15A Estimation of Error Components Principles of Econometrics, 3rd Edition

  45. Appendix 15A Estimation of Error Components Principles of Econometrics, 3rd Edition

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