51 pointScenario 38-4. Consider the following OLS model estimating the effect of job tenure and IQ on worker salary:SALARY ==-49.11.86 × YEARS +0.57 × IQi.(29.1) (1.41)(0.35)Refer to Scenario 38-4. This model is an example ofa natural experiment.a singular regression.a control experiment.a multiple regression.

This problem involves regression since it involves a model to predict the dependent variable,…

Answered: 5 1 point Scenario 38-4. Consider the…

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 4E

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Suppose that a researcher, using wage per hour data on 250 randomly selected male workers and 280 female workers, estimates the following OLS regression wage - 12.68+2.79xMale (0.18) (0.84) R = 0.06, s-3.10 where Male is a dummy variable that takes the value 1 if the worker is male and 0 if female: s represents the standard error of the regression and in brackets homoskedastic std errors are reported. The researcher wants to find the gender pay gap as percentage bf the wagé per hour of women. According to this information the gender pay gap on average against women is approximately -22% -25% -38% O-16%
13. Collinearity in a multiple regression analysis Suppose you want to examine the effects of a training program on future earnings using the following model: earn98= 4.64 +2.376train +0.371earn96 +0.366educ- 1.86 age +2.534 married (1.14) (0.43) (0.016) (0.062) (0.013) (0.4) where earn 98- 1998 earnings, in thousands of dollars train -1 if the individual participated in the training program, and =0 otherwise earn 96- 1996 earnings, in thousands of dollars educ years of education age = age, in years married-1 if the individual is married, and -0 otherwise Suppose that there is a high degree of correlation (but not perfect) between earnings in 1996, education, age, and marital status. True or False: We should be concerned about this high degree of correlation because it affects our ability to reliably estimate the impact of the training program on 1998 earnings, T. True False
Suppose the researcher considers the following model : Wage = Bo+B,Female + u, and runs OLS, using wage data on 250 randomly selected male workers and 280 female workers. The researcher has obtained the estimated equation as Wage = 15 (1.00) 3 Female, R = 0.05. (0.5) In the equation, Wage is measured in dollars per hour, Female is a binary variable that is equal to 1 if the person is a female and O if the person is a male. The numbers in the parentheses are the standard errors of the coefficients. Which Statement is NOT correct? The coefficient of Female, -3.00, is statistically significant at 5%. O The p-value for the test that Ho: B = 0, H : B 0 is less than 0.05. This regression may suffer omitted variable bias. R Since insignificant. is too low, the wage difference between men and women is The researcher can increase R- by including more regressors in the model.
Oil Recovery A U.S. state's Bureau of Economic Geology published a study on the economic impact of using carbon dioxide enhanced oil recovery (EOR) technology to extract additional oil from fields that have reached the end of their conventional economic life. The following table gives the approximate number of jobs for the citizens that would be created at various levels of recovery. Percent Recovery (%) 20 40 80 100 Jobs Created (Millions) 6 6 12 18 Find the regression line. j(r) = Use the regression line to estimate the number of jobs (in millions) that would be created at a recovery level of 50%. million jobs
Consider the following regression model: wage-Bi+Bamalerpumalexedu Buedutu, where wage is the hourly wage measured in dollars: male is a dummy variable for males edu is the years of education: maleedu is the interaction of male and edu variables. The parameter estimates for B parameters are P-1.27: B1.29: Br-0,16: Be-0.82. What is the predicted marginal effect of years of education for males?
We know that discrimination exists. It influences wages, but also many other dimensions over the life cycle which affect wages indirectly. I run OLS regression with variables wage, age, female and degree. The dependent variable is log(wage) and we replace the variables female and degree with the interaction term. However, discrimination is not included among the observed regressors. Given that omitting confounding variables from regression model can bias the coefficient estimates, omitting discrimination would lead to biased results. Could you please help me provide an example of how unobserved gender discrimination can affect my OLS estimates. [Hint: think about ways in which discrimination can invalidate OLS assumptions.].
10. Residual analysis Consider a regression of y on several independent variables, and the resulting predicted values of the dependent variable. The residual for the ith observation Consider a data set for a large sample of professional basketball players. Each observation contains the salary, as well as various performance statistics such as points, rebounds, and assists for each player. Suppose a regression of salary on all performance statistics is run, and the residuals are obtained. The player with the lowest (most negative) resid represents which of the following? (Assume the regression reasonably predicts salaries in most cases.) The most fairly paid player relative to her on-court performance The most overpaid player relative to her on-court performance The highest-paid player, regardless of her on-court performance The most underpaid player relative to her on-court performance
Consider the following log-wage regression results for women (W) and men (M) where wages are predicted by schooling (S) and age (A). wW = 2.23 + 0.077Sw + 0.017Aw and wM = 2.33 + 0.0745SM + 0.026AM. Sample means for the variables by gender are: women average a logged wage of 3.90, 12.7 years of schooling, and 40.8 years-old; men average a logged wage of 4.53, 14.2 years of schooling, and 43.9 years-old. Decompose the raw difference in average logged wages using the Oaxaca-Blinder decomposition. Specifically, decompose the raw difference into the portion due to differences in schooling, differences in age, and the portion left unexplained, possibly due to gender discrimination.
Consider the regression model Y₁ = BX; +u; Where u; and X; satisfy the assumptions specified here. Let ẞ denote an estimator of ẞ that is constructed as ß = Show that ẞ is a linear function of Y₁, Y2,..., Yn. where Y and X are the sample means of Y; and X;, respectively. Show that ẞ is conditionally unbiased. 1. E (Y;|X1, X2,..., Xn) = 1 -B (X ₁ + X 2 + ... + Xn) Х answer these part correctly + ... + Y) +X₂+ ... + Xn) = B 2. E (B|×₁, ×2,..., Xn) = E | (X1, X2,..., Xn) BX; ☑ BX BY
Consider the following ANOVA table for a multiple regression model relating housing prices (in thousands of dollars) to the number of bedrooms in the house and the size of the lot on which the house was built (in square feet). There were 90 total observations. Estimated Price = 24,838.74 + 2339.88 (Bedrooms) + 0.2685 (Lot Size) ANOVA df Regression 2 382,993.4921 Residual 87 627,839.9910 Total 89 1,010,833.4831 SS MS 191,496.7461 26.5358 7216.5516 F Significance F 1.0066E-09 What percent of variation in housing prices is explained by the number of bedrooms and lot size? Round your answer to two decimal places.
Discuss the FIVE (5) importance of adding error term in the regression model.
4. The model below explains CEO salary (salary) expressed in thousands of pounds as a function of a firm' s sales in million pounds (sales), the return on a firm's equity (roe), and the industry in which the firm is operating. There are two industries in the sample, services and consumer goods. These industries are captured respectively by two dummy variables, service and goods. The regression based on a sample of 209 firms is as follows: = 4.5 + 0.27sales + 0.015roe - 0.08service (0.24) (0.03) (0.05) (0. 05) L1090 Introduction to Econometrics R-squared = 0.35 SSR(Residual Sum of Squares) = 42.9 a. Interpret the coefficient of service. b. In the regression model, the consumer goods as an industry is omitted from the model. Explain the implications of adding the binary variable goods to the regression model.

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