Panel Data Models By Mai Thanh, Jin Lulu

15.1 A Microeconomic Panel Open The file includes 3580 observations with a panel structure comprising 5 time series observations (1982, 1983, 1987, 1988) on 716 individuals (women). Open 2/48

If the file is not a panel data structure, it will be instructive to examine—we need to create a panel data structure. Go to Proc/Structure/Resize Current Page… 1 2 3 ID use to identify each woman You can also use YEAR You can also use 1982 You can also use 1988 We have same number of time-series observations on each woman –the panel is balanced one 3/48

Collect the variables into a group 1 2 3 4/48 Company name

Each individual has 5 time series 716 individuals variables 5/48

15.2 Pooled Least Squares Pooled least squares estimates of the wage equation for women. Command: ls lwage c educ exper exper2 tenure tenure2 black south union Information on the # of cross sections and # of time periods 6/48

15.2.1 Cluster-robust standard errors To obtain pooled least squares estimates with Cluster-robust standard errors, go to Quick / Equation Estimate, then modify the Panel Options window as follows: Pooled LS Traditional Standard Errors Pooled LS Cluster-Robust Standard Errors 1 2 3 White period allows for correlation over periods for each individual 7/48

Result compare Pooled LS Cluster-Robust Standard Errors Pooled LS Traditional Standard Errors Pooled LS Cluster-Robust Standard Errors Same Different 8/48

15.3.1 Fixed effects estimation   Two ways to estimate for the fixed effects model: Including dummy (indicator) variables for each individual --Least squares dummy variable estimator “subtracting out” the intercepts prior to estimation --fixed effects estimator 9/48

15.3.1 Least squares dummy variable estimator Fixed effects model estimation with In(WAGE) as the dependent variable and explanatory variables @expand(id) educ exper exper2 tenure tenure2 black south union. Open nls_panel10.wfl. Command: ls lwage @expand(id) educ exper exper2 tenure tenure2 black south union Creates dummy variables EViews' response This is a message occurs if explanatory variables in the model are perfect collinearity. Estimation fail because some variables are time invariant and time-variant variables are collinear with the dummy variables. In this case, BLACK, EDUC, and SOUTH are constant over time for each individual. 10/48

Do fixed effects model estimation again, after dropping the offending variables EDUC, BLACK SOUTH. Command: ls lwage @expand(id) exper exper2 tenure tenure2 union. 11/48

Test the equality of the fixed effects (dummy variable coefficients), if the intercepts are equal for all individuals, then there are no fixed effects. Go to View / Coefficient Diagnostics / Wald – Coefficient Restrictions. The null hypothesis of equal intercepts is rejected at the 1% level of significance 12/48

15.3.2 Fixed effects estimator   Use the fixed effects command that is in the Panel Options window. It does all the transforming of the series into deviation form automatically. Go to Quick / Estimate Equation 2 3 1 13/48

Average of fixed effects Estimator result Average of fixed effects 14/48

1 2 3 Retrieving the fixed effects To locate the spreadsheet having the number of fixed effects, go to View / Fixed / Random Effects / Cross-section Effects. 1 2 3 The spreadsheet for the fixed effects for each of the 10 firms Dummy variable coefficients Least squares dummy variable estimator A comparison with the dummy variable coefficients reveals that they are not the same. The difference is that EViews has expressed them in terms of deviations form the mean of 0.434687 that was reported on the output 15/48

Null hypothesis: no fixed effects—equal intercepts Testing the fixed effects, go to View / Fixed / Random Effects Testing / Redundant Fixed Effects – Likelihood Ratio Null hypothesis: no fixed effects—equal intercepts The null hypothesis of equal intercepts is rejected at the 1% level of significance 16/48

15.3 Fixed effects estimation of complete panel Do fixed effects estimation of complete 716 panel with both traditional standard errors and Cluster-Robust standard errors. SOUTH can be included since complete panel contains who moved into and out of the South during the sample period; BLACK and EDUC continue to be omitted because of their collinearity with the dummy variables. Open nls_panel.dta, Go to Quick / Estimate Equation. . 1 2 Fixed Effects with Traditional Standard Errors Fixed Effects with Cluster-Robust Standard Errors 3 4 17/48

Estimator result Fixed Effects with Cluster-Robust Standard Errors Fixed Effects with Traditional Standard Errors Fixed Effects with Cluster-Robust Standard Errors Same Different 18/48

Computing marginal effects   19/48

15.4 Random effects   20/48

Random effects estimation takes into account variation between individuals as well as variation within individuals, which means it is possible to include EDUC and BLACK in the model. Go to Object / New Object / Equation. 1 Random Effects with Traditional Standard Errors Random Effects with Cluster-Robust Standard Errors 3 2 4 21/48

  the proportions of total error variance attributable to each of the components Random Effects with Traditional Standard Errors   Company name 22/48

Random Effects with Cluster-Robust Standard Errors Different Same   23/48