1. Panel Data Models
By Mai Thanh, Jin Lulu

2. 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
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3. 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
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4. Collect the variables into a group1 2 3
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Company name

5. Each individual has 5 time series 716 individuals variables
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6. 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
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7. 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
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8. Result compare Pooled LS Cluster-Robust Standard Errors
Pooled LS Traditional Standard Errors
Pooled LS Cluster-Robust Standard Errors
Same
Different
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9. 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
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10. 15.3.1 Least squares dummy variable estimator
Fixed effects model estimation with In(WAGE) as the dependent variable and explanatory educ exper exper2 tenure tenure2 black south union. Open nls_panel10.wfl. Command: ls 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.
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11. Do fixed effects model estimation again, after dropping the offending variables EDUC, BLACK SOUTH. Command: ls exper exper2 tenure tenure2 union.
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12. 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
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13. 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
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14. Average of fixed effects
Estimator result
Average of fixed effects
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15. 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 that was reported on the output
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16. 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
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17. 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
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18. Estimator result Fixed Effects with Cluster-Robust Standard Errors
Fixed Effects with Traditional Standard Errors
Fixed Effects with Cluster-Robust Standard Errors
Same
Different
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19. Computing marginal effects
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20. 15.4 Random effects
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21. 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
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22. the proportions of total error variance attributable to each of the components
Random Effects with Traditional Standard Errors
Company name
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23. Random Effects with Cluster-Robust Standard Errors Different Same
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