ii. Interpret both slope coefficients of the estimated OLS regression of wage (in rupees) on education (in years) and experience (in years). What is the overall predicted wage change if education and experience change by one year each? = Wage 30000+1500 education + 500 experience What can you infer about the shape (U-shape/inverse U-shape) of the relationship between job performance (the number of tasks completed per day) and hours of sleep a person gets from the following estimated OLS regression? At what level of sleep does the job performance peak? Job Performance 20+5 hours of sleep-0.25 (hours of sleep)2

ii. Interpret both slope coefficients of the estimated OLS regression of wage (in rupees) on education (in years) and experience (in years). What is the overall predicted wage change if education and experience change by one year each? = Wage 30000+1500 education + 500 experience What can you infer about the shape (U-shape/inverse U-shape) of the relationship between job performance (the number of tasks completed per day) and hours of sleep a person gets from the following estimated OLS regression? At what level of sleep does the job performance peak? Job Performance 20+5 hours of sleep-0.25 (hours of sleep)2

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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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
In this question we look at the relation between the logarithm of weakly earnings and years of education. Using data from the national longitudinal study of youth, we find the following results for a regression of log weekly earnings and years of education, experience, experience squared and an intercept: Log (earnings) = 4.016 + 0.092 . educi + 0.079 .expei + 0.002 . experi 2 ( 0.222) ( 0.008) ( 0.025) (0.001) c). Labour economist studying the relation between education and earnings are often concerned about what they call “ability bias”. Suppose that individuals differ in ability, and that the correct specification of the regression function is one that includes ability: log ( earnings )I = β1 + β 2 ⋅ educI + β 3 ⋅ experI − β 4 ⋅ experI2 + β5 ⋅ abilityI + εI . In this regression, what do you expect the sign of β5 (the coefficient on ability) to be?
Question 3 Consider the following regression: In (wage) = Bo + B₁ ln(age) + 3₂ ln(education) + u, where wage is measured in dollars, age is measured in years, and education is measured in years. The coefficient 3₁ is best interpreted as the increase in wage from increasing age by one percent, all else constant the percentage increase in wage from increasing age by one percent, all else constant O the percentage increase in wage from increasing age by one year, all else constant the percentage increase in wage from increasing age by one year, divided by 100, all else constant the effect on wage of increasing age by one year, all else constant Question 4 Consider the following multivariate regresssion model: y =B₁ + B₁x₁ + B₂x₂ + ... + ßkxk+u, and suppose that the errors are homoskedastic. The variance of the multivariate model error, u, will decrease as the level of each explanatory variable increases, conditional on the levels of all of the other explanatory variables. True O False
Suppose that in a linear regression model hourly wages are explained as a function of gender, where the dummy variable female takes on a value of 1 if the worker is a female and O otherwise: SRL : wâge 25 + 2 * female Which interpretation(s) are correct for this SRL? An additional female worker increases hourly wages by 2 dollars on average. Men earn on average 25 dollars per hour. Women earn on average two dollars more per hour than men. Women are the baseline group.
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.].
The modern interpretation of regression says that regression... O A. studies the statistical dependence of a regressor on a regressand with the view of estimating the mean value of the former in terms of the fixed values of the latter. O B. studies the causal link between the dependent variable and independent variable (or variables). C. studies the statistical dependence of a regressand on a regressor (or regressors) with the view of estimating the precise relationship between variables in order to forecast changes in the relationship between the variables. O D. studies whether there is statistical independence of a dependent variable in relation to an independent variable with the view of estimating statistical dependence. E. studies the statistical dependence of an outcome variable on an exogenous variable (or variables) with the view of estimating the average response of the regressand for given values of predictor variables.
How should I interpret the coefficients on a regression with a naural log of a dependent variable? Ex: Ln(wage)=B1+B2Experience+B3Male...+Ui
13. Collinearity in a multiple regression analysis Suppose you want to examine the effects of a training program on future earnings using the following model: earn98= 4.64 +2.376train +0.371earn96 +0.366educ- 1.86 age +2.534 married (1.14) (0.43) (0.016) (0.062) (0.013) (0.4) where earn 98- 1998 earnings, in thousands of dollars train -1 if the individual participated in the training program, and =0 otherwise earn 96- 1996 earnings, in thousands of dollars educ years of education age = age, in years married-1 if the individual is married, and -0 otherwise Suppose that there is a high degree of correlation (but not perfect) between earnings in 1996, education, age, and marital status. True or False: We should be concerned about this high degree of correlation because it affects our ability to reliably estimate the impact of the training program on 1998 earnings, T. True False
3. Boyoung is writing a paper about the effect of Sunday liquor sales on drunk driving. She has panel data on which states allow liquor to be sold on Sunday in which years and wishes to estimate a difference-in-differences model. She writes the following regression equation: Year DUIRate;; = atate +a? + BTreatmentt +yControl;t + Eit i Which of the following changes does Boyoung need to make? a) She should include a constant in the regression equation. b) She should not include controls because they're already accounted for by fixed effects. c) She should include a time trend instead of time-fixed effects. d) None of the above are changes she needs to make.
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.
You have data on all individuals in Sweden, including their family size (i.e. their number of children) and their earnings. You want to estimate the effect of family size on earnings, but you suspect your regression will be biased as you do not have information on preferences for how many children people want to have. a) You have information on gender of all children. You have heard that peoplewho have two children of the same gender are more likely to have a third child. Would you be able to use gender composition of the first two children as an instrument for family size? Discuss the assumptions needed for such a model (assume treatment effects are constant). Set up the assumptions mathematically and explain in words what they mean. b) How would you test if the assumptions in b) hold? Is it likely that they hold? (Assume gender composition of the first two children is the only instrument you have access to.)
If you are interested in investigating themarginal effect of the percentage change of lot size on the changeof house price, then in your linear regression analysis you should apply thelogarithm transformation on: Select one: a. The house price variable. b. Both the house price variable and the lot size variable. c. The lot size variable. d. The bedroom variable. e. Both the bedroom variable and the house price variable.