TABLE 14.1 Fixed Effects Estimation of the Scrap Rate Equation Dependent Variable: log(scrap) Independent Variables Coefficient (Standard Error) d88 -.080 (.109) d89 -.247 (.133) grant - 252 (.151) - 422 (,210) grant_1 Observations 162 Degrees of freedom R-squared 104 .201
This question assumes that you have access to a statistical package that computes standard errors robust to arbitrary serial
(i) For the pooled OLS estimates in Table 14.1, obtain the standard errors that allow for arbitrary serial correlation (in the composite errors, vit = ai + uit) and heteroskedasticity. How do the robust standard errors for educ, married, and union compare with the nonrobust ones?
(ii) Now obtain the robust standard errors for the fixed effects estimates that allow arbitrary serial correlation and heteroskedasticity in the idiosyncratic errors, uit. How do these compare with the nonrobust FE standard errors?
(iii) For which method, pooled OLS or FE, is adjusting the standard errors for serial correlation more important? Why?
 alt="TABLE 14.1 Fixed Effects Estimation of the Scrap Rate Equation
Dependent Variable: log(scrap)
Independent Variables
Coefficient (Standard Error)
d88
-.080
(.109)
d89
-.247
(.133)
grant
- 252
(.151)
- 422
(,210)
grant_1
Observations
162
Degrees of freedom
R-squared
104
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