PLEASE INTERPRET THE FOLLOWING AS PER THE QUESTION

a) Using OLS on the full sample estimate a model for log(wage) using explanatory variables educ, abil, exper, nc, west, south and urban. Report the estimated effect of a years education and its standard error.

b) Now assume any observation with wage 20 to have wage = 20 so that everyone from part (a) still remains in the sample, but if they earn above $20 an hour, then it is assumed as equal to $20. Run the regression from part (a) and comment on the coefficient on educ.

c) Using the sample in part (b) apply a censoring regression. Does the censoring correction appear to recover the effect of educ in the full population?

d) Now drop all observations with wage 20, so that everyone remaining in the sample earns less than $20 an hour. Run the regression from part (a) and comment on the coefficient on educ.

e) Using the sample in part (d) apply a truncated regression [with the upper truncation point being log(20)]. Does the truncated regression appear to recover the effect of educ in the full population?

\#\# Newton-Raphson maximisation, 13 iterations \#\# Return code 8: successive function values within relative tolerance limit (reltol) \#\# Log-likelihood: 43.50902 on 9 Df \#\# Call: \#\# truncreg(formula = lwage educ + abil + exper +nc+ west + \#\# south + urban, data = trunhtvlwage, point =0, direction = "left") \#\# \#\# BFGS maximization method \#\# 35 iterations, oh:0m:0s #g(H)1g=0.00104 \#\# \#\# \#\# \#\# Coefficients : \#\# Estimate std. Error t-value Pr(>t) \#\# (Intercept) 1.35254640.16005038.4508