First five questions are true or false questions.

Please explain why true or false for each question.

1. Measurement error in the dependent variable is more serious than measurement error in the explanatory variables.Table 1. STATA results from OLS estimation of the earnings model Source SS of MS Number of obs = 100 F( 2, 97) = 16.47 Model | 2057.5037 2 1028.75185 Prob > F = 0.0000 Residual | 6059.71269 97 62.4712648 R-squared = 0.2535 Adj R-squared = 0.2381 Total | 8117.21639 99 81.9920847 Root MSE = 7.9039 wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] Educ | 1.435782 .321546 4.47 0.000 .7976026 2.073962 Exper | .328525 .0658247 4.99 0.000 .1978813 .4591687 _cons | -11.91922 4.750254 -2.51 0.014 -21.34716 -2.491275 Figure 1. Wage residuals versus education Residuals -20 10 15 20 gradeTable 2. Pairwise correlations | grade exper wage grade | 1.0000 exper | -0.3665 1.0000 wage | 0.2485 0.3163 1.0000 Table 3. STATA results with squared OLS residuals as the dependent variable Source | 5S df MS Number of obs = 100 F( 5, 94) = 1.97 Model | 498933.661 5 99786.7323 Prob > F = 0.0901 Residual | 4759291.93 94 50630.7652 R-squared = 0.0949 Adj R-squared = 0.0467 Total | 5258225.59 99 53113.3898 Root MSE = 225.01 res2 | Coef. Std. Err. t P>|t] [95% Conf. Interval] Grade | -7.357599 79.35932 -0.09 0.926 -164.9274 150.2122 Exper | -23.67913 16.87954 -1.40 0.164 -57.19386 9.835591 Grade 2 | -1.048003 2.223082 -0.47 0.638 -5.461984 3.365978 Exper 2 | .270444 .162453 1.66 0.099 -.0521102 .5929982 Exper* Grade | .5788711 .7165818 0.81 0.421 -.8439188 2.001661 cons 108.2517 582.867 0.19 0.853 -1049.044 1265.548#6. (50 pts) Consider the earnings model: Wage, = 1 + ByExpen + BEduct + w, 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 N = 100 males in a given year. Use the tables and figures to answer the following questions: a) (5 pts) Using the results in Table 1, summarize the overall goodness of fit of the model. Do the signs of the coefficients match your explanations? Explain.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.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 / 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.#7. (25 pts) Provide brief answers to the following three questions. a) (5 pts) Write down an AR(1) process for the error terms and describe how to perform a Durbin-Watson test for autocorrelation.b) (10 pts) Re-specify the earnings model in question #6, to test the hypothesis that men in a labor union earn more on average than men that are not members of a labor union. How would you test this hypothesis? Draw an appropriate figure of the regression model to support your test.c) (10 pts) Re-specify the earnings model in question #6, to test the hypothesis that men in a labor union earn more for each additional year of experience than men that are not members of a labor union. How would you test this hypothesis? Draw an appropriate figure of the regression model to support your test