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Exercise 4.14 How much does education affect wage rates? This question will explore the issue further. That data file cps4 small.dat contains 1000 observations on hourly wage rates, education, and other variables from the 2008 Current Population Survey (CPS). a) Construct histograms of the WAGE variable and its logarithm, In(WAGE). Which appears more normally distributed? b) Estimate the linear regression WAGE = B1 +B₂2 EDUC + e and log-linear regression In(WAGE) - B1+ B₂EDUC + e. What is the estimated return to education in each model? That is, for an additional year of education, what percentage increase in wages can the average worker expect? c) Construct histograms of the residuals from the linear and log-linear models in b), and the Jarque-Bera test for normality. Does one set of residuals appear more compatible with normality that the other? d) Compare the R² of the linear model to the "generalised" R² for the log-linear model. Which model fits the data better? e) Plot the least squares residuals from each model against EDUC. Do you observe any patterns? which functional form would you use? Explain.

Exercise 4.14 How much does education affect wage rates? This question will explore the issue further. That data file cps4 small.dat contains 1000 observations on hourly wage rates, education, and other variables from the 2008 Current Population Survey (CPS). a) Construct histograms of the WAGE variable and its logarithm, In(WAGE). Which appears more normally distributed? b) Estimate the linear regression WAGE = B1 +B₂2 EDUC + e and log-linear regression In(WAGE) - B1+ B₂EDUC + e. What is the estimated return to education in each model? That is, for an additional year of education, what percentage increase in wages can the average worker expect? c) Construct histograms of the residuals from the linear and log-linear models in b), and the Jarque-Bera test for normality. Does one set of residuals appear more compatible with normality that the other? d) Compare the R² of the linear model to the "generalised" R² for the log-linear model. Which model fits the data better? e) Plot the least squares residuals from each model against EDUC. Do you observe any patterns? which functional form would you use? Explain.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Exercise 4.14 How much does education affect wage rates? This question will explore the issue further. That data file cps4 small.dat contains 1000 observations on hourly wage rates, education, and other variables from the 2008 Current Population Survey (CPS). a) Construct histograms of the WAGE variable and its logarithm, In(WAGE). Which appears more normally distributed? b) Estimate the linear regression WAGE = B1 +B₂2 EDUC + e and log-linear regression In(WAGE) - B1+ B₂EDUC + e. What is the estimated return to education in each model? That is, for an additional year of education, what percentage increase in wages can the average worker expect? c) Construct histograms of the residuals from the linear and log-linear models in b), and the Jarque-Bera test for normality. Does one set of residuals appear more compatible with normality that the other? d) Compare the R² of the linear model to the "generalised" R² for the log-linear model. Which model fits the data better? e) Plot the least squares residuals from each model against EDUC. Do you observe any patterns? which functional form would you use? Explain.
Transcribed Image Text:Exercise 4.14 How much does education affect wage rates? This question will explore the issue further. That data file cps4 small.dat contains 1000 observations on hourly wage rates, education, and other variables from the 2008 Current Population Survey (CPS). a) Construct histograms of the WAGE variable and its logarithm, In(WAGE). Which appears more normally distributed? b) Estimate the linear regression WAGE = B1 +B₂2 EDUC + e and log-linear regression In(WAGE) - B1+ B₂EDUC + e. What is the estimated return to education in each model? That is, for an additional year of education, what percentage increase in wages can the average worker expect? c) Construct histograms of the residuals from the linear and log-linear models in b), and the Jarque-Bera test for normality. Does one set of residuals appear more compatible with normality that the other? d) Compare the R² of the linear model to the "generalised" R² for the log-linear model. Which model fits the data better? e) Plot the least squares residuals from each model against EDUC. Do you observe any patterns? which functional form would you use? Explain.
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