Scenario 38-3. Consider the following statistical model of workers salaries and years of job tenure, where e; stands for the random error:SALARY = 0 + ẞ1 × YEARS; + ej.Refer to Scenario 38-3. Suppose that some people are simply more capable than others. When would the estimate of ẞ, be unbiased?0000As long as the model is applied to observational dataAs long as the underlying data comes from a natural experimentAs long as ability and tenure are not correlatedAs long as ability and tenure are correlated

In the statistical model you've provided, whereSALARYi=β0+β1×YEARSi+ei , the estimate of β1…

Answered: Scenario 38-3. Consider the following…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

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Assume that the dummy variables Marriedal if married (0 if not married), Sex=1if male (0 if female), and that you estimate the equation: loglwage) le + B,'Married + B'Sex + B,(Married Sex). The estimated log wage for an unmarried woman can be estimated by: Bo
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.
an economist makes an assumption that each year of additional education causes future wages to rise by 11 percent. in this model, if a person with 12 years of education makes $24,500 per year, then a person with 4-year college degree would earn per year. Note:- Do not provide handwritten solution. Maintain accuracy and quality in your answer. Take care of plagiarism. Answer completely. You will get up vote for sure.
The following equation has been used to estimate wages: ln (Y) = ln (Yo) + b2EDU + b3 EXPER + b4 EXPER2 + e where Y is income, Y0 is income of someone with no education or experience, EDU is years of education and EXPER is experience in the field. If you suspect males earn higher wages than females and that the wage difference increases with education how would you adjust the econometric model to estimate wages? OPTIONS - a. include a binary variable for gender, MALE b. include an interaction term equal to MALE* EDUC c. include an indicator variable for MALE and one for FEMALE d. include a binary variable for MALE and an interaction term equal to MALE * EDUC
Suppose now that that Y denotes the annual salaries (in thousands of dollars) for US citizenswho are in their 40s and 50; and X is an indicator (dummy) variable: X = 1 if that workergraduated from college and X=0 otherwise. Suppose the average annual salary for this age groupis $45,000 and the estimate for β is β~ = 15 with s.e. = 5.b. What is the upper and lower bound of a 95-percent confidence interval for β~ ?c. In a credible research design attending college would be randomly assigned to a largesample of potential students. Propose such a research design. What are the potential ethicalissues? d. The central problem with drawing credible casual inferences is that the counterfactual cannot be observed. What is the counterfactual in this case? What is a good strategy forobtaining the counter-factual? What are the ethical issues of doing this? b-d please!!!
Consider the following regression estimates (FN2) Linear regression belavg abvavg female married _cons b. 1 wage O C. 5 O d. 4 Robust Coef. Std. Err. 3047845 3150202 2820787 -1.063254 .0693348 -2.751963 .9686236 .2612646 6.699098 .2889831 Number of obs F(4, 1255) Prob > F R-squared Root MSE t P>|t| -3.49 0.001 0.22 0.826 -9.76 0.000 3.71 0.000 23.18 0.000 |||||||||| = = .45606 6.132155 = -1.661197 -.5486894 -3.305361 = = [95% Conf. Interval] 1,260 56.35 0.0000 0.1121 4.3987 assume that MLR 1-6 hold. In the regression above, how many coefficients (including the constant) are statistically significant at the 1% level? a. 3 -.4653108 .687359 -2.198565 1.481187 7.266042
Suppose we are interested in evaluating the impact on wages of being a college graduate and of residing in California. To this end we define the following dummy variables if college graduate if not college graduate coll = ncoll = if not college graduate if college graduate 1 cali = if residing in California S 1 if not residing in California ncali = if not residing in California if residing in California and for wages denoting yearly wages, we estimate the following model of returns to education wages = B1 coll+ B2ncoll + B3 cali + B4(coll x cali) + e where E[e|coll, cali] = 0. Which parameter or combination of parameters measures the increase in expected wages from obtaining a college degree (comparing to not having a college degree) when residing California? O a. None of the other options is correct O b. Bi – B2 O c. B1 – B2 + ß4 O d. B4 O e. Bi + B3 – ßi – B4 O f. Bi + B4 – ß2 – B4 Clear my choice
Q1. State the property of an estimator that gives a correct estimate of the population parameter of interest, but only on average. Q2. State the property of ian estimal or thal converges to the populalion paaameter of interest as the sample size goes to infinity. Q3. Why is an efficient estimator a desirable property of the OLS estimator? Q4. Stare the assumption(s)under the classical linearregression model giving rise to a biased standard error of the coefficient estimates when violated.
Consider the following regression estimates (ID6) Source Model Residual Total wage educ exper exper2 _cons inverse u-shaped SS C. linear d. exponential 1927.87673 5232.53756 7160.41429 df -.0046123 -3.96489 642.625576 3 522 10.0240183 525 Coef. Std. Err. .5953429 .0530251 .268287 .0368969 .000822 .7521526 MS 13.6388844 Number of obs F(3, 522) Prob > F R-squared Adj R-squared Root MSE t P>|t| 0.000 11.23 7.27 0.000 -5.61 0.000 -5.27 0.000 = .4911741 .1958023 -.006227 -5.442508 = = = 526 64.11 0.0000 0.2692 0.2650 3.1661 [95% Conf. Interval] .6995118 .3407717 where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience squared (exper* exper). According to these estimates, what is the functional form of the relationship between experience and predicted wage when we hold education constant? a. u-shaped b.i -.0029975 -2.487272
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
Consider the population regression of log earnings [Y;, where Y,= In(Earnings,;)] against two binary variables: whether a worker is married (D₁, where D₁;= 1 if the th person is married) and the worker's gender (D2;, where D₂;= 1 if the th person is female), and the product of the two binary variables Y₁ = Po+B₁D₁+P₂D2i + P3 (D₁¡ × D₂i) + Hi- The interaction term: O A. indicates the effect of being married on log earnings. B. does not make sense since it could be zero for married males. C. allows the population effect on log earnings of being married to depend on gender. D. cannot be estimated without the presence of a continuous variable.
A commonly used functional form for wage equations is InW = a + bR + cR2, where W is the annual wage, R is the fatal accident rate, and the other job and employee characteristics are omitted for simplicity. 20 20 1. Derive an expression for the value of a statistical life. 2. Why is it important to include R2 in the wage equation?

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