Suppose wage (Y) is determined by years of schooling (yrsc) and sociability (soc) the following causal model: Y = Bo + B₁yrsc + ß₂ soc+u, where u captures all other unobservable variables and is uncorrelated with both years of schooling and sociability. Assume ß¿ > 0 for i Furthermore, assume cov(yrsc, soc) > 0. What can you conclude about the short regression model: Y = ß* + B₁yrsc + e where = 0, 1, 2.

Suppose wage (Y) is determined by years of schooling (yrsc) and sociability (soc) the following causal model: Y = Bo + B₁yrsc + ß₂ soc+u, where u captures all other unobservable variables and is uncorrelated with both years of schooling and sociability. Assume ß¿ > 0 for i Furthermore, assume cov(yrsc, soc) > 0. What can you conclude about the short regression model: Y = ß* + B₁yrsc + e where = 0, 1, 2.

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

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 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
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
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
a. b. Given the equation, L = 2r + 3k + 8(q-¹/2k¹/2) dL/dr = ? dL/dk = ?
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.].
II. Performance Appraisal method of performance appraisal, show all possible pairs Using the Paired-Comparison for the following employees. Note: Show how many all possible pairs and compute for the number of possible pairs = N=? Abraham, Nathaniel, Desiree, Gregorio, Angelica, Daniel, Jasper
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
TRUE OR FALSE. When heterosdasticity is present OLS estimators are still BLUE.
Consider a simple OLS model that regresses college GPA (gpa) on high school GPA (hsgpa) and parental income (pincome, in $1000 per month). B₁ + B₂hsgpa + B3pincome + e. Empirical estimation using observations generates the following STATA output. gpa = Source Model Residual Total gpa hsgpa pincome _cons SS 1273.27041 671.879965 df Coef. Std. Err. .6866603 .0103571 .3033177 .0066108 -.6697239 .0358777 Which statement below is incorrect? MS Number of obs F(2, 3997) Prob > F R-squared Adj R-squared Root MSE t P>|t| = = = 4,000 [95% Conf. Interval] A college student whose parents do not have an income is expected to earn a negative GPA. High school GPA is a significant contributing factor to college GPA. Besides high school GPA and parental income, there exist other factors that affect college GPA. Every $1000 increase in parental income is expected to raise a student's college GPA by 0.303.
When estimating the equation: i Acrimerate =B+B Aunemployment + Au, i 0 The estimate of B, is only causal if there is no correlation between Acrimerate; 1 True False and Au; i
(Yi, X1i, X2i) satisfy the assumptions of the attachment. You are interestedin β1, the causal effect of X1 on Y. Suppose that X1 and X2 are uncorrelated.You estimate β1 by regressing Y onto X1 (so that X2 is not included in theregression). Does this estimator suffer from omitted variable bias? Explain.