Scenario 38-1. Consider the following OLS model applied to the data for workers wages and years of schooling, where e; stands for the residual:WAGE = -10.7+3.16 x SCHOOL + e.(20.7) (1.35)Refer to Scenario 38-1. Suppose that the residual term has a zero mean and is uncorrelated with the independent variable. According to the estimated model, each year of schoolingdecreases a worker's wage by $3.16.000decreases a worker's wage by $10.7.increases a worker's wage by $3.16.increases a worker's wage by $10.7.

The estimated model given is:\[ \text{WAGE} = -10.7 + 3.16 \times \text{SCHOOL} + e \]This model…

Answered: Scenario 38-1. Consider the following…

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

Chapter7: Production Economics

Section: Chapter Questions

Problem 1.3CE

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(20.7) (1.35) Scenario 38-1. Consider the following OLS model applied to the data for workers wages and years of schooling, where e; stands for the residual: WAGE = -10.7+ 3.16 x SCHOOL + e. Refer to Scenario 38-1. Suppose that the residual term has a zero mean and is uncorrelated with the independent variable. Suppose that the standard error is 1.35. This means that we can be 95 percent confident that the true wage benefit of a year of schooling 000 is equal to $8. is equal to $0.46. lies between $0.46 and $5.86. lies between $8 and $13.4.
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%
14. Estimating the variance of the error term Suppose you are interested in looking at the determinants of a worker's wage, and use the following econometric model to do so: wage=β0+β1 educ+u wage=β0+β1 educ+u where educ = years of education u = error term You take a sample of 120 individuals and collect data on each person's wage and level of educational attainment. An unbiased, observable estimator of the variance of the error term (σ2?2) is σˆ2?^2
A certain standardized test measures students' knowledge in English and math. The English and math scores for 10 randomly selected students were recorded and analyzed. The results are shown in the computer output. Predictor Coef SE Coef t-ratio Constant -124.13 78.712 0.046 Math 1.223 0.1966 6.220 0.000 S = 34.55 R-Sq = 82.8% R-Sq (Adj) = 83.5% Which of the following represents the standard deviation of the residuals? O 1.223 34.55 78.712 124.13
wages = B1 + B2educ + ßzexper + e where wages denotes hourly wages. We estimate the regression in R and obtain the output ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.0 1.0 2 0.0455 * ## educ 0.5 0.5 1 0.3173 ## exper 2.0 0.5 4 6.33e-05 *** ## --- ## Signif. codes: ****' 0. 001 '**' 0.01 **' 0.05 ' 0.1 ' ' 1 Build a 90% confidence interval for B3 using a normal approximation. (Use that if Z ~ N(0, 1) and z1-a satisfies P(Z > z1-a) = a, then zo9 = 1.28, zo95 = 1.64, Z0.975 = 1.96, zo.99 = 2.33, and z0.995 = 2.58). Oa. [2 – 1.64 x (0.5), 2 + 1.64 x (0.5)] O b. [2 – 1.28 × (0.5), 2 + 1.28 x (0.5)] c. [2 – 1.28 x (0.5)², 2 + 1.28 × (0.5)²] O d. [2 – 1.64 × (0.5)², 2 + 1.64 × (0.5)²] O e. [2 – 1.96 × (0.5)², 2 + 1.96 × (0.5)²] O f. [2 – 1.96 × (0.5), 2 + 1.96 × (0.5)]
Suppose that the government wants to estimate the effect of PKH on children's height usine WHO's height-for-age metric. Using administrative and surveydata, a researcher evaluates the impact of PKH using the following specification: (image insert) 1. What is the expected sign of beta? What assumption needed to obtain an unbiased estimate of beta 1? 2. is the assumption sensible? what are the consequence of violating the assumption! Discuss whether the estimated coefficient beta 1, positively or negatively bias? 5. do you think adding variables would fulfill the assumption?
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 the December, 1969, American Economic Review (pp. 886-896), Nathanial Leff reports thefollowing least squares regression results for a cross section study of the effect of age composition onsavings in 74 countries in 1964:log S/Y = 7.3439 + 0.1596 log Y/N + 0.0254 log G - 1.3520 log D1 - 0.3990 log D2 (R2= 0.57)log S/N = 8.7851 + 1.1486 log Y/N + 0.0265 log G - 1.3438 log D1 - 0.3966 log D2 (R2= 0.96)where S/Y = domestic savings ratio, S/N = per capita savings, Y/N = per capita income, D1 = percentage ofthe population under 15, D2 = percentage of the population over 64, and G = growth rate of per capitaincome. Are these results correct? Explain..
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
does the simple regression of log (price) on log (nox) produce an upward or a downward biased estimator of β1?
TRUE OR FALSE. When heterosdasticity is present OLS estimators are still BLUE.
23. An education economist estimated the wage equation to determine the rate of return to an additional year of education and obtained ln(WAGE) = -0.5220+0.1075×EDUC. it is anticipated that ability ( attributes, such as intelligence, creativity, perseverance and industriousness) is positively correlated with UDUC. If a measure of a bility is omitted from the model the estimated return to education A. Is biased downwards B. Is biased upwards C. Cannot be estimated D. All the above

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