This preview shows pages 1–3. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: Introduction to Applied Econometrics Midterm 2 Do not turn page until told to do so! Name: ID & TA: Richard Walker, Winter 2010 Introduction to Applied Econometrics Richard Walker, Winter 2010 Midterm 2 Name: ID & TA: (50points) Answer all questions, using the spaces provided. There is room for doodling at the back. There is also a formula sheet and a table of sample correlation coefficients. I am interested in the determinants of earnings, for example education, gender and different kinds of intelligence (specifically numerical and verbal). Using the same dataset weve seen in class, I first run the following regression: LGEARN = 1 + 2 S + u (#1) where LGEARN is the natural log of EARNINGS (which in turn is hourly earnings in dollars) and S is years of schooling. The regression results are given in the relevant table at the end (regression #1). Note that Ive deliberately omitted some of the results (indicated by * ). 5pts 1. Use the regression results provided to determine the results of an Ftest (at the 5% significance level) of the explanatory power of regression #1. Explain what youre doing. I then add two new regressors to my equation: LGEARN = 1 + 2 S + 3 ASVAB02 + 4 ASVAB03 + 5 ASVAB04 + u (#2) where ASVAB02 is the individuals score on an arithmetic reasoning test, ASVAB03 the individuals score on a word knowledge test and ASVAB04 an individuals score on a paragraph comprehension test. These variables are meant to capture the numerical (ASVAB02) and verbal (ASVAB03 & ASVAB04) dimensions of intelligence referred to above. The results are in the relevant table at the rear (regression #2). Again, some numbers have been withheld (indicated by * )....
View Full
Document
 Spring '08
 Witte
 Macroeconomics, Econometrics

Click to edit the document details