Final Exam Econometrics

Final Exam Econometrics - Kate Brown Dr. Cuellar...

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Unformatted text preview: Kate Brown Dr. Cuellar Econometrics Final a). reg wage exper exper2 educ looks female Source | SS df MS Number of obs = 1260 -------------+------------------------------ F( 5, 1254) = 64.15 Model | 5569.95985 5 1113.99197 Prob > F = 0.0000 Residual | 21777.4793 1254 17.3664109 R-squared = 0.2037 -------------+------------------------------ Adj R-squared = 0.2005 Total | 27347.4392 1259 21.7215561 Root MSE = 4.1673 ------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- exper | .257418 .0384844 6.69 0.000 .181917 .332919 exper2 | -.0039446 .0008609 -4.58 0.000 -.0056335 -.0022557 educ | .4095647 .0464471 8.82 0.000 .3184421 .5006872 looks | -.4049482 .1751361 -2.31 0.021 -.7485403 -.0613562 female | -2.503287 .255191 -9.81 0.000 -3.003935 -2.002639 _cons | .3519805 .8714727 0.40 0.686 -1.357725 2.061686 ------------------------------------------------------------------------------ b) Overall significance of the model : Adj R-squared = 0.2005: meaning that 20.05% of the regression is explained by the model. exper | .257418 t-stat 6.69 exper2 | -.0039446 t-stat -4.58 educ | .4095647 t-stat 8.82 looks | -.4049482 t-stat -2.31 female | -2.503287 t-stat -9.81 At first it seems that looks have a negative affect on wage. . display invFtail(5,1258,.05) 2.2212133 F( 5, 1254) = 64.15 c) 5 10 15 20 Standardized residuals 2 4 6 8 10 Fitted values The graph above compares the Standardized residuals to the fitted values. It would appear there is a negative trend. The graph is heteroskedastic. I would assume that in the lower income jobs that the effect of beauty may not have much of an impact. d) Yes, there is an obvious outlier at 4.5,18 as seen in the graphed residual analysis below: 20 40 60 80 Residuals 2 4 6 8 10 Fitted values e) Cooks Distance measures how much one particular observation alone affects the regression estimates. After a regression is run, you perform a Cooks Distance test to recognize how far away that point is from the means of the independent variables and dependent variable. In our case we observe influential points as outliers in the Hammer mesh and Biddle data. Cooks Distance: 4/n : Deletes one Observation at a time and measures the change in slope. display 4/1260 .0031746 Observations seen with a change in slope over .0031746 impact our model and could be problematic. f) . list looks wagehat D if D> .003 +-----------------------------+ | looks wagehat D | |-----------------------------| 57. | 3 4.788664 .0030575 | 267. | 4 4.436476 .0120295 | 337. | 3 5.230033 .009749 | 348. | 2 4.360388 .1786339 | 404. | 4 6.236249 .0044739 | |-----------------------------| 431. | 3 8.454038 .0066791 | 474. | 3 8.62073 .0173239 | 524. | 3 5.912581 .0110585 | 615. | 2 7.840163 .0036605 | 627. | 1 9.883369 .0149926 | |-----------------------------| 649. | 2 7.975298 .0329545 | 679. | 1 10.14575 10....
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Final Exam Econometrics - Kate Brown Dr. Cuellar...

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