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BONUS QUESTION. (5 pts) Describe an economic situation (other than the one in my lecture notes) where panel data will allow you tease out two impacts that would not be possible using only cross-section or only time-series data.#T. [25 pita] Provide brief answers to the following that questions. I) hi {5 p13} Write down an AR\") process for the error terrrn and describe how to perform a DurhinWataon teat for autumn-relation. {ll} pot] lite-specify the earnings model in question #5. to test the hypothesis that men in a labor union earn more on average than men that are not members of a labor union. I-lo'ur would you test this hypothesis? Draw an appropriate figure of the regression model to support your test. tlptsj lie-specify the earnings model in question #6. to test the hypothesis that men in a labor union earn more for each ariditionai year of experience than men that are not members of a labor union. How would you test this hypothesis? Draw an appropriate gure of the regression model to support your test. d) (10 pts) Based on your answers to parts (b) and (c), describe a procedure to obtain the efficient estimators for the coefficients. e) (5 pts) Use the results in Table 2 to discuss the severity of the multicollinearity and the likely impacts on the OLS results in Table 1. f) (10 pts) Show how to perform a f test that the EXPER and EDUC coefficients are equal. Do you have all the necessary information to complete the test? If so, complete the test.#6. (50 pts) Consider the earnings model: Wage; = 1 + 2Exper; + ByEduc; + uj, where Wage is the measured in dollars per hour, Exper is work experience in years, and Educ is the number of years of schooling. Tables 1-3 and Figure 1 show the OLS regression results for W = 100 males in a given year. Use the tables and figures to answer the following questions: b) (10 pts) Interpret the residual pattern in Figure 1. What conclusion do you draw? And based on that conclusion, what are the impacts on the OLS estimates in Table 1? Explain. c) (10 pts) Using the results in Table 3, perform White's test for heteroscedasticity. Be sure to carefully set up the null and alternative hypotheses and draw a conclusion.1. Measurement error in the dependent variable is more serious than measurement error in the explanatory variables. 2. Autocorrelation biases the coefficients towards zero. 3. Severe multicollinearity will bias the coefficient estimates. 4. Ordinary least squares is a special case of generalized least squares. 5. The "runs test" for autocorrelation is immune from Type I errors