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First regression: wage = ß1 + B2educ + ßzexper + B4(educ X exper) + e Second regression: wage = ß1 + Bzeduc + Bzexper + e %3D where wage denotes hourly wages. We estimate both regressions in R and obtain the output:

First regression: wage = ß1 + B2educ + ßzexper + B4(educ X exper) + e Second regression: wage = ß1 + Bzeduc + Bzexper + e %3D where wage denotes hourly wages. We estimate both regressions in R and obtain the output:

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)
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 8E
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First regression: wage = B1 + B2educ + B3 exper + B4(educ x exper) + e Second regression: wage = B1 + Bzeduc + B3exper + e where wage denotes hourly wages. We estimate both regressions in R and obtain the output: ## Coefficients: ETTI ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 1.50 1.50 0.3173 ## (Intercept) 2.0 1.0 0.0455 * ## educ 0.50 0.50 1 0.3173 ## educ 0.5 0.5 1 0.3173 ## exper 1.50 0.50 0.0027 ** ## exper 2.0 0.5 4 6.33e-05 *** ## educ:exper 0.10 0.05 0.0455 * ## ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1 ## Signif. codes: O '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 where in the left panel, the row educ:exper refers to the estimates for B4 in the first regression. Suppose we are interested in testing the null hypothesis that the marginal returns to education do not depend on experience using an F-test. Recall the F-statistic is given by ((SSER - SSE)/J)/(SSEu/(n – K)). Which of the following statements is true? O a. We do not have enough information to compute the F-statistic (SSER - SS EU)/J)/(SSEu/(n – K)) for the desired hypothesis. O b. The F-statistic ((SSER - SSEU)/J)/(SS Ey/(n – K)) for the desired hypothesis equals 16. O c. The F-statistic ((SSER – SS EU)/J)/(SSEy/(n – K)) for the desired hypothesis equals 4. O d. The F-statistic ((SSER – SS Ey)/J)/(SSEy/(n – K)) for the desired hypothesis equals (0.1)² O e. The F-statistic ((SSER – SSEU)/J)/(SSE/(n – K)) for the desired hypothesis equals 0.1. Clear my choice
Transcribed Image Text:First regression: wage = B1 + B2educ + B3 exper + B4(educ x exper) + e Second regression: wage = B1 + Bzeduc + B3exper + e where wage denotes hourly wages. We estimate both regressions in R and obtain the output: ## Coefficients: ETTI ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 1.50 1.50 0.3173 ## (Intercept) 2.0 1.0 0.0455 * ## educ 0.50 0.50 1 0.3173 ## educ 0.5 0.5 1 0.3173 ## exper 1.50 0.50 0.0027 ** ## exper 2.0 0.5 4 6.33e-05 *** ## educ:exper 0.10 0.05 0.0455 * ## ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1 ## Signif. codes: O '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 where in the left panel, the row educ:exper refers to the estimates for B4 in the first regression. Suppose we are interested in testing the null hypothesis that the marginal returns to education do not depend on experience using an F-test. Recall the F-statistic is given by ((SSER - SSE)/J)/(SSEu/(n – K)). Which of the following statements is true? O a. We do not have enough information to compute the F-statistic (SSER - SS EU)/J)/(SSEu/(n – K)) for the desired hypothesis. O b. The F-statistic ((SSER - SSEU)/J)/(SS Ey/(n – K)) for the desired hypothesis equals 16. O c. The F-statistic ((SSER – SS EU)/J)/(SSEy/(n – K)) for the desired hypothesis equals 4. O d. The F-statistic ((SSER – SS Ey)/J)/(SSEy/(n – K)) for the desired hypothesis equals (0.1)² O e. The F-statistic ((SSER – SSEU)/J)/(SSE/(n – K)) for the desired hypothesis equals 0.1. Clear my choice
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