Ran Wang
Uni-variable regression:
\[ y_i=\beta_0+\beta_1x_i+u_i,(i=1,...,n) \]
Stata Code:
reg y x
Multi-variable regression:
\[ y_t=\beta_0+\beta_1x_{1i}+\beta_2x_{2i}+\beta_3x_{3i}+u_i,(i=1,...,n) \]
Stata Code:
reg y x1 x2 x3
E 7.1
Data: CPS08
( a ) Run a regression of AHE on AGE. What is the estimated intercept? What is the estimated slope?
( b ) Run a regression of AHE on AGE, Female and Bachelor. What is the estimated effect of age on earnings? Construct a 95% condence interval for the coefficient on Age in the regression.
E 7.1
Data: CPS08
( c ) Are the results from the regression in ( b ) substantively different from the results in (a) regarding the effects of Age and AHE? Does the regression in (a) seem to suffer from omitted variable bias?
( d ) Bob is a 26-year-old male worker with a high school diploma. Predict Bobs earnings using the estimated regression in (b). Alexis is a 30-year-old female worker with a college degree. Predict Alexisearnings using the regression.
E 7.2
Data: Teacher Ratings
( a ) Run a regression of Course_Eval on Beauty. Construct a 95% confidence interval for the effect of Beauty on Course_Eval.
( b ) Consider the various control variables in the data set. Which do you think should be included in the regression? Using a table like Table 7.1, examine the robustness of the condence interval that you constructed in (a). What is a reasonable 95% confidence interval for the effect of Beauty on Course_Eval?