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

See similar textbooks

Similar questions

9
Find the required linear model using least-squares regression. The following table shows the number of operating federal credit unions in a certain country for several years. 2011 2012 2013 2014 4497 4323 4156 3976 Year Number of federal credit unions (a) The linear model for these data is y = (Round to the nearest tenth as needed.) (a) Find a linear model for these data with x = 11 corresponding to the year 2011. (b) Assuming the trend continues, estimate the number of federal credit unions in the year 2018. X + 2015 (b) The estimated number of credit unions for the year 2018 is (Round to the nearest integer as needed.) 3807
Look at the following regression table where the dependent variable is the demand for illegal massage services in a city in the United States. Specifically, the dependent variable is the number of customers per hour (Models 1 and 2) or per day (Models 3 and 4). (a) Explain why the coefficient for Population/1,000 in Model 2 is very different from the one in Model 4?(b) Can you reject H0 in Model 1 if H0 : βP opulation/1,000 = 0.01, H1 : βPopulation/1,000 6= 0.01, and α = 0.01?
Solve the second question in regression analysis
A researcher feels that global warming may be reducing average rainfall in Perlis for the past 10 years. The researcher is interested in making predictions for future rainfall. The 10 years data of the rainfall in Perlis is shown in Table 4. Table 4 Year Amount (cm) 2009 40 2010 39 2011 41 2012 29 2013 32 2014 30 2015 15 2016 10 2017 11 2018 20 (a) Without using any software, construct a simple linear regression model for the above data. (b) Predict the rainfall for the year 2019 and 2020.
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?
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.
A researcher has estimated the following multiple regression model to investigate the determinants of capital structure in an emerging market based on data from 2016. LEV = 1.32 – 0.10TANG - 0.28PROFIT + 0.19GROWTH + e (0.92) (0.03) (0.25) (0.04) Residual sum of squares = 200Total sum of squares = 620Number of Observations = 90Standard errors of the coefficients are given in parentheses. The variables are:LEV = Leverage (total debt to total assets).TANG = Tangibility (net fixed assets to total assets). PROFIT = Profitability (net income to total assets). GROWTH = Firm growth (Percent change in sales). e = residual For each independent variable slope coefficient, test the null hypothesis that it is equal to zero against the alternative hypothesis that it is not equal to 0. The critical t value is 1.96 at the 5% significance level for a two-tailed test.
The Pilot Pen Company has decided to use 15 test markets to examine the sensitivity of demand for its new product to various prices, as shown in the following table. Advertising effort was identical in each market. Each market had approximately the same level of business activity and population. Complete the following worksheet and then estimate the demand function for Pilot's new pen using a linear regression model. Test Market i 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Total O 1.577 Regression Parameters Estimations Slope (B) Intercept (a) O 1.761 O 105.432 O 0.028 Price Charged (cents) Xi 50 50 55 55 60 60 65 65 70 70 80 80 90 90 40 1.902 O 0.032 980 What is the standard error of the estimate (se)? Quantity Sold (Thousands of Pens) O Yes O No Yi 20 21 19 19.5 20.5 19 16.5 15 14 15.5 13 14 11.5 11 17 What is the estimate of the standard deviation of the estimated slope (st)? 246.5 TiYi 1,000 1,050 1,045 1,072.5 1,230 1,140 1,072.5 975 980 1,085 1,040 1,120 1,035 990 680 I₂² 2,500 2,500…
Consider a simple linear regression model with predictor variable x and response variable y, where the regression line is represented by the equation y = β0 + β1x. If β0 = -5 and β1 = 3, what is the predicted value of y for a given x = 4?
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)
Consider the following linear regression model: Yi = B1 + B2x2i + B3x3i + ei of = o²r What is the weight to be used for generalized (or weighted) least-squares estimation? Select one: 1 1 Ob. 1 Oc. 1 od.