Four hundred driver's license applicants were randomly selected and asked whether they passed their driving test (Pass; = 1) or failed their test (Pass; = 0); data werealso located on their gender (Male; = 1 if male and = 0 if female) and their years of driving experience (Experience;, in years). The following table summarizes theresults from several probit, logit and linear probability, models.Dependent variable: Pass.Regression ModelRegressorProbitLogit(2)LPMProbitProbitLogit(5)LPM(1)(3)(4)(6)(7)0.0330.0440.0080.043Experience(0.157)- 0.171(0.263)- 0.019(0.019)0.805(0.202)(0.007)(0.011)(0.002)- 0.627- 0.073(0.035)- 0.339Male(0.162)(0.304)Male x Experience0.7171.0560.7771.2882.1920.903Constant(0.128)(0.223)(0.037)(0.125)(0.245)(0.024)Use the results in columns (4) through (6) to answer the following questions.Compute the estimated probability of passing the test for men and for women.ProbitLogit(5)LPMGroup(4)(6)Men0.830Women0.900(Round your responses to three decimal places)

Answered: Image /qna-images/answer/3403a806-b860-48e9-b0f1-790eecdd3784.jpg

Four hundred driver's license applicants were randomly selected and asked whether they passed their driving test (Pass; = 1) or failed their test (Pass; = 0); data we also located on their gender (Male; = 1 if male and = 0 if female) and their years of driving experience (Experience;, in years). The following table summarizes the results from several probit, logit and linear probability models. Dependent variable: Pass. Regression Model Regressor Logit (2) Logit (5) Probit LPM Probit LPM Probit (1) (3) (4) (6) (7) 0.033 0.044 0.008 0.043 Experience (0.007) (0.011) (0.002) (0.157) - 0.171 (0.263) - 0.019 (0.019) 0.805 (0.024) (0.202) - 0.339 - 0.627 -0.073 Male (0.162) (0.304) (0.035) Male x Experience 0.717 1.056 0.777 1.288 2.192 0.903 Constant (0.128) (0.223) (0.037) (0.125) (0.245) Use the results in columns (4) through (6) to answer the following questions. Compute the estimated probability of passing the test for men and for women. Probit (4) Logit (5) LPM Group (6) Men 0.830 Women 0.900 (Round your responses to three decimal places)

Four hundred driver's license applicants were randomly selected and asked whether they passed their driving test (Pass; = 1) or failed their test (Pass; = 0); data we also located on their gender (Male; = 1 if male and = 0 if female) and their years of driving experience (Experience;, in years). The following table summarizes the results from several probit, logit and linear probability models. Dependent variable: Pass. Regression Model Regressor Logit (2) Logit (5) Probit LPM Probit LPM Probit (1) (3) (4) (6) (7) 0.033 0.044 0.008 0.043 Experience (0.007) (0.011) (0.002) (0.157) - 0.171 (0.263) - 0.019 (0.019) 0.805 (0.024) (0.202) - 0.339 - 0.627 -0.073 Male (0.162) (0.304) (0.035) Male x Experience 0.717 1.056 0.777 1.288 2.192 0.903 Constant (0.128) (0.223) (0.037) (0.125) (0.245) Use the results in columns (4) through (6) to answer the following questions. Compute the estimated probability of passing the test for men and for women. Probit (4) Logit (5) LPM Group (6) Men 0.830 Women 0.900 (Round your responses to three decimal places)

Microeconomic Theory

12th Edition

ISBN:9781337517942

Author:NICHOLSON

Publisher:NICHOLSON

Chapter19: Externalities And Public Goods

Section: Chapter Questions

Problem 19.12P

See similar textbooks

Similar questions

With Panel Data, if we assume that the individual effects vi are not correlated with the regressors Xit (i.e. E(vi|Xit) = 0), which one of the following statements is correct: The Fixed Effects estimator is not consistent. Both the OLS and the Random Effects estimators are not consistent. The OLS estimator is not consistent, but the Random Effects estimator is consistent. The OLS and the Random Effects estimator are consistent. All of the above. None of the above
ASSISTANCE REQUIRED FOR PART "e" i- vi ONLY, please. Also can you please show step by step how to get null and alternative in the hypothesis? A car company wants to know the monthly sales made in ($000), based on the brand of vehicle.The data collected was entered on a MINITAB spreadsheet for analysis. Exhibit II below was subsequently generated. Exhibit 2 Model N Mean Median Tri.Mean Std Dev S.E. Mean Hyundai 23 109 135 107.64 * 1.34 Toyota 27 165 124 143.65 9.5 ** a) Determine the values of * and **. b) Give the unbiased point estimate for the average monthly earning from Hyundai Vehicles.c) Give the unbiased point estimate for the variance of monthly earning from Hyundai Vehicles.d) Assuming normality, construct and interpret a 90% confidence interval for the average monthly earning from Toyota Vehicles. e) You are required to test at the 5% level of significance the hypothesis that the average monthly earnings on Hyundai Vehicles is equal to $110,000 versus the…
We want to estimate the treatment effect of D ony via a linear regression model y = bo+b1x +rD+u.where x is an exogenous controi variable. If the value of the treatment variable Dis randomly assigned. there O a. the OLS estimate of r is biased due to sample selection. Ob.the OLS estimate ofris unbiased. Oche error u is homoskedastic. Od.the OLS estimator is BLUE.
Describe the important characteristics of the variance of a conditional distribution of an error term in a linear regression. What are the implicationsfor OLS estimation?
Suppose we use the linear probability model to estimate the effect of household income on the willingness to avail a loan product. Given a regression equation: Willingness = 22.34 -0.04HouseholdIncome, with HouseholdIncome measured in hundreds of pesos. How is -0.04 interpreted?
Which of the following is true of heteroskedasticity? a) The R-squared statistic is affected by the presence of heteroskedasticity b) Heteroskedasticty causes inconsistency in the Ordinary Least Squares estimators c) The OLS estimators are not the best linear unbiased estimators if heteroskedasticity is present d) It is not possible to obtain F statistics that are robust to heteroskedasticity of an unknown form
What is sampling? Explain the differences between probability and nonprobability samples and identify the various typesof each
The management of a large firm would like to know something about employee morale and the other variables to which it may be related. To study these relationships, each employee in a simple random sample of 27 employees is interviewed and tested to obtain information concerning morale level and the average number of satisfying extra work social contacts experienced during a week. Whether or not the duties the employee usually performs are appropriate to the job title is also determined. The dependent variable Y is measure of morale, the quantitative independent variable, X1 is average number of social contacts, and the following dummy variable coding is used to quantify the qualitative variable: X2 = 1 if yes, X2 = 0 if no. The model including dummy variable and the interaction term was being estimated and a part of computer outputs is below: beta std error t p-value 95% CI for B Lower bound Upper bound Constant 260 134.308…
Suppose that the following binary dependent models. The model is based on the driving test of the 400 randomly selected driver’s license applicants. Y=1 if passed the test, or 0 otherwise, and X1 is years of experiences, X2 is the years of educations. Logit: P(Y=1/X) = F(0.563+ 0.040X1+ 0.057X2) What are the probabilities of passing the test for a person with 10 years of experiences and 10 years of educations in each model?
Please no written by hand The assumption of normally distributed errors means that... A. errors can be ignored when doing regression modelling. B. the OLS estimators can also be assumed to be normally distributed since they are a linear functions of the errors. C. the OLS estimators can also be assumed to be normally distributed since they are BLUE. D. the OLS estimators can also be assumed to be normally distributed since they are minimum variance. E. the regression model will not be subject to specification error.
A large school district is reevaluating its teachers' salaries. They have decided to use regression analysis to predict mean teacher salaries at each elementary school. The research has come up with the following prediction equation: Y = $18012.24 + 1432.37X1 - 4.07 X2 where X1 = Yrs Exp and X2 = Yrs Exp2 (a) If a teacher has 7 years of experience, what is the expected salary? (b) If teacher has 10 years of experience, what is the expected salary?
In exercise 1, the following estimated regression equation based on 10 observations was presented. y^=29.1270+.5906x1+.4980x2Here SST=6724.125, SSR=6216.375, sb1=.0813, and sb2=.0567. a) Compute MSR and MSE. b) Compute F and perform the appropriate F test. Use α=.05. c) Perform a t test for the significance of β1. Use α=.05. d) Perform a t test for the significance of β2. Use α=.05.