ECMT1020 Introduction to Econometrics 2022S2 Assignment
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ECMT1020 Introduction to Econometrics
2022S2
Assignment
Instructions
1. There are 10 questions in this assignment each worth 3 marks. The assignment has a maximum of 30 marks and accounts for 15% of your final grade.
2. This assignment entails the use of econometric models and statistical tools in an economic application. You will use a statistical software, Stata, to analyze data on educational attainment and wages.
3. Your assigned dataset is the CSV spreadsheet EAWE# .csv, where # is the last digit of your University of Sydney SID. Please use your assigned dataset to answer the questions. Write your dataset number on the front page of your work. Using the wrong dataset will be reviewed as a potential case of Academic Dishonesty.
4. Answer all the questions. Show all numerical answers to 3 decimal places. Carry out all tests using a 5% level of significance. You may include Stata outputs in your answers, but your own interpretations and explanations are necessary for earning marks.
5. 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 accor- dingly.
6. You will have to type your answers. Handwritten submissions will not be accepted.
7. The assignment is anonymously marked. Save your answers in a pdf file2named 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.
8. Submit the pdf file through Turnitin under the Canvas module ‘Assignment’. Late sub- mission is subject to a penalty of 5% per calendar day. Work submitted more than 10 days after the due date will receive a mark of zero.
Data Description
You will use a subset consisting of 500 observations of the Educational Attainment and Wage Equations (EAWE) dataset to answer the questions. The description of the dataset and contained variables can be found in Appendix B on p.565–569 of the textbook.
Questions
1. Fit an educational attainment function using your dataset. 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. Perform a F test of the explanatory power of the equation you obtained in Question 1. Calculate the F statistic using R2 and verify it is the same as the F statistic in your Stata output.
3. Regress the logarithm of EARNINGS on S and EXP . Interpret the regression results, perform t tests on the coefficients and F test of the explanatory power of the model.
4. Regress logarithm of EARNINGS on S , EXP , MALE , ETHHISP and ETHBLACK . Interpret the regression results and perform t tests on the coefficients.
5. Redo Question 4 making ETHBLACK the reference category. What are the impacts of change of reference on the interpretation of the coefficients and the statistical tests (t tests of the coefficients and F test of the model)?
6. Define a slope dummy variable as the product of MALE and S . Regress the logarithm of EARNINGS on S , EXP , ETHHISP , ETHBLACK , MALE, and the slope dummy variable. Interpret the equation and perform appropriate statistical tests (t tests of the coefficients and F test of the model). Is the effect of education on earnings different for males and females?
7. The composite measure of cognitive ability, ASVABC, in the dataset was constructed as a weighted average of the scores of tests of arithmetic reasoning, ASVABAR, word know- ledge, ASVABWK , and paragraph comprehension, ASVABPC, with ASVABAR being given double weight. Show mathematically that, when fitting the educational attainment function
S = β 1 + β2SM + β3SF + β4ASVABC + u,
instead of the model using the individual scores
S = β 1 + β2SM + β3SF + γ1ASVABAR + γ2ASVABWK + γ3ASVABPC + u,
one is implicitly imposing the restrictions γ1 = 2γ2 and γ1 = 2γ3 . Perform a test of these restrictions using your dataset.
8. Fit a wage equation with EARNINGS as the dependent variable and S , EXP and MALE as the explanatory variables. Perform a Goldfeld-Quandt test for heteroskedasticity in the S dimension.
9. Fit a wage equation using the same specification as in Question 8. Perform a White test for heteroskedasticity.
10. Perform an OLS regression of the logarithm of hourly earnings on S , EXP , ASVABC , MALE , ETHBLACK and ETHHISP using your dataset and an IV regression using SM , SF, and SIBLINGS as instruments for ASVABC . Perform a Durbin-Wu-Hausman test to evaluate whether ASVABC appears to be subject to measurement error.
2022-10-21