ECMT1020 Introduction To Econometrics Assignment

Instructions

1. This assignment accounts for 15% of your final grade. There are 10 questions in this assignment with 5 marks worth each, and the full mark of the assignment is 50. Please attempt all questions.
2. This assignment entails the use of econometric models and statistical tools in economic application. You will use statistical software, Stata, to analyze the educational attainment and wage equations data.
3. Please save your answers in a pdf fifile1 named 123456789.pdf where 123456789 is your 9-digit SID. Do not put your name in your work or anywhere in your submission. Do not include a cover sheet.
4. The dataset you will use is in the Excel spreadsheet EAWE#.xlsx, where # is the last digit of your University of Sydney SID. Please use your assigned data set to answer the questions and write your data set number on the front page of your work. Using the wrong data set will be reviewed as a potential case of Academic Dishonesty.
5. Answer all the questions. Show all numerical answers to 2 decimal places if necessary. When you are asked to ‘perform a test’, you should write explicitly the null hypothesis of the test, and state clearly how you make testing decisions and make conclusions. Please carry out all tests using a 5% level of significance.
6. You should include Stata procedures and outputs in your answers, and your own interpretations and explanations are necessary for earning marks. Please type your answer in a document. We do not accept handwritten solutions.
7. When answering the questions, please keep your statements concise as well as accurate. Excessively long responses indicate a lack of understanding and will be penalized accordingly.
8. Submit one pdf file through Turnitin under the Canvas module ‘Assignment’. Late submission is subject to a penalty of 5% of total 50 marks, which is 2.5 marks, per day. Work submitted more than 10 days after the due date will receive a mark of zero. There are in accordance with 7A in the University Assessment Procedures 2011.

Data Description

You will use a subset consisting of 500 observations of Educational Attainment and Wage Equations (EAWE) dataset to answer the questions. The description of the data set and contained variables can be found in Appendix B on p.565–569 of the textbook (also provided in a separate pdf file).

In particular, note that EXP and TENURE in your dataset are, respectively, the number of years at work and the number of years spent working with the current employer.

We define a new variable PREVEXP = EXP?TENURE. PREVEXP, thus defined, is the total work experience with previous employers, and will be used in some of the questions. We use LGEARN to denote the logarithm of EARNINGS.

Questions

1. Fit an educational attainment function using your data set. Regress S on ASVABC, SM and SF, and interpret the regression results. Perform t tests on the coefficients of the variables in the education attainment function.
2. Use the Box and Cox procedure (Steps 1–3) described on p. 211 of the textbook to evaluate whether the dependent variable of a wage regression of EARNINGS on S and EXP should be linear or logarithmic based on your dataset.
3. Following up on the previous question, now defifine demeaned variables S? = S ? S and EXP? = EXP ? EXP, where S and EXP denote the sample mean of S and EXP. Regress EARNINGS or LGEARN, depending on your result in the previous question, on S? and EXP? and interpret the regression output. In particular, how is the interpretation of the two slope coefficients in this regression different from the regression where regress are S and EXP instead?
4. Consider the following two regressions:
   1. Regress LGEARN on S and PREVEXP;
   2. Regress LGEARN on S, TENURE and PREVEXP.

   Before running any regressions in your software, what is your expectation of the relative magnitude of the coeifficients of PREVEXP in the two fitted regressions? Explain why you expect so. Then, run these two regressions in Stata and see if the result confifirms your expectation.

5. Explain how you could get the same OLS estimate of the coefficient of PREVEXP in the multiple regression in Question 4(a) using “purged regressions”. Implement your procedure in Stata and show the results are matched.
6. Consider a regression of LGEARN on S, EXP, TENURE, PREVEXP, ASVABC, ETHBLACK and ETHHISP. Can you run this regression in Stata? If yes, please report your output. If not, please explain why this is the case.
7. Regress LGEARN on S, PREVEXP, TENURE, ASVABC, ETHBLACK and ETHHISP. Explain how you would conduct an F test for testing the coefficients in front of PREVEXP and TENURE being equal. Please perform the test and interpret your result.
8. Regress LGEARN on S, EXP, TENURE, ASVABC, ETHBLACK and ETHHISP. Perform a t test on the coeffiffifficient of TENURE. Please explain why such a t test is a test of the same restriction described in Question 7. Verify that the same result is obtained from the previous F test and the t test here.
9. How do you interpret the coeffiffifficients of ETHBLACK and ETHHISP in the fitted regression in Question 8?
10. Add an intercept dummy MALE and a slope dummy defifined as the product of MALE and S in the regression in Question 8, and then run the regression again. Interpret the coefficients before these two dummy variables. Is the effect of education on earnings different for males and females?