What are the difficulties in estimating the following model? Use as much detail as possible in answering this question while considering the Gauss-Markov assumptions and OLS estimator. Economic productivity = β0 + β1Unemployment + β2Innovation + θiControls + ei Where unemployment is the average unemployment rate of a country and innovation is an index of R&D performance.

What are the difficulties in estimating the following model? Use as much detail as possible in answering this question while considering the Gauss-Markov assumptions and OLS estimator. Economic productivity = β0 + β1Unemployment + β2Innovation + θiControls + ei Where unemployment is the average unemployment rate of a country and innovation is an index of R&D performance.

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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Heteroscedasticity Stigler and Friedland (1983) conducted a study to determine whether the separation of company control from company ownership affects company profits. Using data from 69 companies in the United States, the authors estimate the following model: profiti = α + β1asseti + β2management_controli + ei Where i is company and: profiti = annual profit (in million dollar) asseti = company asset (in million dollar) management_controli = dummy variable that is worth one if the control of the company is held by the manager The regression results are presented in the table in the picture a. Explain whether the statements below are TRUE, FALSE, or CANNOT BE DETERMINED. "If there is a heteroscedasticity problem, the confidence interval of the OLS estimator is not valid." b. Determine the 95% confidence interval for the parameter 2, what can you conclude?
Bxi + €i, where €; are independently and Consider a simple linear regression model Y; = identically distributed with mean 0 and variance o?, and i = 1,..., n. Note: this model does not have a intercept term. Derive the Best Linear Unbiased Estimator (BLUE) for B. Denote this by BBLUE. Make sure to state why this is the BLUE.
Let Y = β0 + β1x + E be the simple linear regression model. What is the interpretation of the least squares estimate for β0? Select one: a. It is an estimate of the expected value of the response variable Y when the explanatory variable X is zero. b. It is an estimate of the change in the expected value of the explanatory variable X for every unit increase in the response variable Y. c. It is an estimate of the change in the expected value of the response variable Y for every unit increase in the explanatory variable X. d. It is an estimate of the expected value of the explanatory variable X when the response variable Y is zero.
1. Consider the following regression model: Fram Risk Score; = Bo + B1 × Health Insurance; + u¿ The Framingham Risk Score predicts 10-year risk of cardiovascular disease based on age, cholesterol levels, blood pressure, blood sugar, use of medication for high blood pressure, and smoking. A higher score means worse overall cardiovascular health. A researcher who collects data and regresses the Fram Risk Score against Health Insurance (= 1 if have insurance) finds that B, 2. Consider the following regression model: Class Average; = Bo + B1 x Office Hours; + u; Class Average is the students' average grade in the class at the end of the term and Office Hours is the number of office hours held by the instructor over the entire term. A researcher who collects data and regresses Class Average against Office Hours finds that, surprisingly, B V A V A
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3. Consider the following regression model: Weekly Hours = Bo + B1 × Wage + uj Weekly Hours is the average number of hours the individual worked over the course of the year and Wage is the individual's average hourly wage over the course of the year. A researcher who collects data and regresses Weekly Hours against Wage finds that B1 > 0. The OLS estimator, B, however, likely suffers from omitted variable bias because those individuals who earn high wages may be driven personalities who would work long hours no matter the wage. Because of this omitted variable bias, it is likely the case that B1_B1. A) В)
A prospective MBA student would like to examine the factors that impact starting salary upon graduation and decides to develop a model that uses program per-year tuition as a predictor of starting salary. Data were collected for 37 full-time MBA programs offered at private universities. The least squares equation was found Y; = -13258.594 + 2.422X;, where X; is the program per-year tuition and Y; is the predicted mean starting salary. To perform a residual analysis for these data, the following results are obtained. of regression have been seriously violated. Residual index plot QQ Plot of Residuals Residuals Residuals 20000- 20000 0. -20000 -20000 a) To evaluate whether the assumption of linearity has been violated, which of the following graph shou be examined? A. Predicted Values vs. Residuals B. Residual index plot C. QQ plot of residuals D. Residuals vs. Progrm Per-Year Tuition ($) b) To evaluate whether the assumption of normality has been violated, which of the following graph…
I estimate a multiple linear regression model for portfolio using market size and value and adding as usual a constant ( to make sure that E(errors)=0 as per the GM assumptions request ). Then I wish to identify monthly effects so I generate a dummy for each month of the year , from January to December included . EViews will Select one: a. send out an error message saying : near singular matrix . This is happening because I fell into the dummy variable trap because the sum of all the dummy variables is at any point in time equal to 1 . As a consequence the OLS estimator is not identified b. send out an error message saying : near singular matrix . This is happening because I fell into the dummy variable trap because the costant (c) multiplies the regressor 1 and the sum of all the dummy variable is at any point in time equal to 1 . As a consequence the model is not identified and no estimator can estimate the betas c. send out an error message saying : near singular…
Square Feet Sum of Bedrooms and Bathrooms Age of the Home Sales Price Square Feet Residual Plot Square Feet Line Fit Plot 1,610 5 70 227,900 800,000 2,146 6 59 284,900 700,000 816 4 70 149,900 FO000 600,000 2,183 6.5 48 309,900 40000 1,046 5.5 64 134,900 500.000 20000 5,183 10.5 21 440,000 400,000 • 2 000 1,150 4 62 150,000 1,000 4,000 5.000 6,000 2000 0 300,000 1,068 70 154,900 4000 0 5,570 7 50 700,000 200,000 6000 0 2,449 6. 53 257,000 100,000 BO00 0 1,950 59 239,900 1000 00 2,630 7.5 73 349,900 Square Feet 1,000 2,000 3,000 4,000 5,000 6,000 Square Feet 2,732 7.5 20 339,900 1,908 5 46 289,000 3,666 6.5 17 399,900 Sum of Bedrooms and Bathrooms Residual Plot Sum of Bedrooms and Bathrooms Line Fit Plot 80000 1,878 7 19 290,000 800,000 2,172 62 278,000 60000 700,000 40000 600,000 SUMMARY OUTPUT 20000 500,000 400,000 Regression Statistics 12 2000 0 Multiple R 0.949366054 300,000 R Square 0.901295904 4000 0 200,000 Adjusted R Square 0.878518035 6000 0 100,000 Standard Error 47571.46177…