Part 3: Interpretation of OLS regression Consider the following OLS regression output which is estimated with Wage2.dta (see tutorial folder on Blackboard) MS 935 36.82 Source ss df Number of obs F (4, 930) Model 22.6467366 4 5.66168416 Prob > F 0.0000 Residual 143.009547 930 .153773706 0.1367 R-squared Adj R-squared Root MSE 0.1330 Total 165.656283 934 .177362188 .39214 lwage Coef. Std. Err. P>|t| (95% Conf. Interval] educ .0400982 .0068351 5.87 0.000 .0266843 .0535122 .0009967 0.000 .005914 .0035899 .0039579 -.0219954 -.0003389 1Q 5.93 .0078701 hours .013037 .0001298 .3240363 0.28 0.783 0.517 0.000 .0291752 hours2 -.0000842 -0.65 .0001706 _cons 5.649044 17.43 5.013117 6.284971 where Iwage is the natural logarithm of monthly wage in US$, educ is years of education, IQ is points on an IQ intelligence test, hours is average weekly hours worked, and hours2 is experience squared (hours * hours). Assume that MLR 1-6 hold. 2 8. Write down the econometric model that this OLS regression estimates. 9. Interpret the educ coefficient. 10. At how many average weekly hours worked is the marginal effect of hours on predicted wage equal to zero? 11. What is the exact effect (in percent) of an increase of 10 years of education on predicted wage? | 12. What is the predicted monthly wage in US$ for a worker with 10 years of education, an IQ score of 100, who works on average 40 hours per week?
Part 3: Interpretation of OLS regression Consider the following OLS regression output which is estimated with Wage2.dta (see tutorial folder on Blackboard) MS 935 36.82 Source ss df Number of obs F (4, 930) Model 22.6467366 4 5.66168416 Prob > F 0.0000 Residual 143.009547 930 .153773706 0.1367 R-squared Adj R-squared Root MSE 0.1330 Total 165.656283 934 .177362188 .39214 lwage Coef. Std. Err. P>|t| (95% Conf. Interval] educ .0400982 .0068351 5.87 0.000 .0266843 .0535122 .0009967 0.000 .005914 .0035899 .0039579 -.0219954 -.0003389 1Q 5.93 .0078701 hours .013037 .0001298 .3240363 0.28 0.783 0.517 0.000 .0291752 hours2 -.0000842 -0.65 .0001706 _cons 5.649044 17.43 5.013117 6.284971 where Iwage is the natural logarithm of monthly wage in US$, educ is years of education, IQ is points on an IQ intelligence test, hours is average weekly hours worked, and hours2 is experience squared (hours * hours). Assume that MLR 1-6 hold. 2 8. Write down the econometric model that this OLS regression estimates. 9. Interpret the educ coefficient. 10. At how many average weekly hours worked is the marginal effect of hours on predicted wage equal to zero? 11. What is the exact effect (in percent) of an increase of 10 years of education on predicted wage? | 12. What is the predicted monthly wage in US$ for a worker with 10 years of education, an IQ score of 100, who works on average 40 hours per week?
Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter4: Eigenvalues And Eigenvectors
Section4.6: Applications And The Perron-frobenius Theorem
Problem 22EQ
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