1 Empirical researchers and policy analysts find it more convenient at times to transform all of the variables in a regression into natural logs to interpret coefficients as constant elasticities. Thusyou transform the quantity of bread, family size, income, price of bread, and price of meat by taking the natural log of each of their values. The population regression equation then looks like:In(Q of Bread) = Bo+31 In(Family Size)+3zln(Family Income)+3aln(P of Bread)+3gln(P of Meat)+eYou run the regression and obtain the following excel output:Regression StatisticsMultiple RR SquareAdjusted R SquareStandard Error0.9200.8460.133Observations35ANOVAdfSSMSFRegressionResidualTotal2.90440.72641.3210.52723.4316Coefficients Standard Error t Stat P-valueInterceptLn Family SizeLn Family IncomeLn Price BreadLn Price Meat-0.6250.427 -1.4660.15320.3760.1093.4510.00170.6950.1345.1950.0000-1.1370.158 -7.2100.00000.6330.1584.010.0004Estimated coefficients under this model are interpreted differently than usual. When the natural log has been applied on all variables (before the estimation), an estimated coefficient, say b,now is interpreted as follows: for every 1% change in the original Xị , this results in an estimated b; % change in the original y.What then is the interpretation of the coefficient on Ln Family Income?Select one:O a.For every additional unit of income, the estimated quantity of bread increases by 0.695O b. For every additional unit of income, the estimated quantity of bread increases by 5.195O c. For every additional 1% of income, the estimated quantity of bread increases by 0.695%O d. For every additional 1% of income, the estimated quantity of bread increases by 5.195%O e. For every additional 1% of natural log income, the estimated natural log quantity of bread increases by 0.695%

Regression refers to the method in statistics that are used to identify the type of relationship…

Empirical researchers and policy analysts find it more convenient at times to transform all of the variables in a regression into natural logs to interpret coefficients as constant elasticities. Thus you transform the quantity of bread, family size, income, price of bread, and price of meat by taking the natural log of each of their values. The population regression equation then looks like: In(Q of Bread) = Bu+3 In(Family Size)+ln(Family Income)+3aln(P of Bread)+3aln(P of Meat)+e You run the regression and obtain the following excel output: Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.920 0.846 0.133 Observations ANOVA 35 df SS MS F Regression Residual Total 2.9044 0.726 41.321 0.5272 3.4316 Coefficients Standard Error t Stat P-value Intercept Ln Family Size Ln Family Income Ln Price Bread Ln Price Meat -0.625 0.427 -1.466 0.1532 0.109 0.134 0.376 3.451 0.0017 0.695 5.195 0.0000 -1.137 0.158 -7.210 0.0000 0.633 0.158 4.01 0.0004 Estimated coefficients under this model are interpreted differently than usual. When the natural log has been applied on all variables (before the estimation), an estimated coefficient, say b, s interpreted as follows: for every 1% change in the original x, this results in an estimated b, % change in the original y. now What then is the interpretation of the coefficient on Ln Family Income? Select one: O a. For every additional unit of income, the estimated quantity of bread increases by 0.695 O b. For every additional unit of income, the estimated quantity of bread increases by 5.195 O c. For every additional 1% of income, the estimated quantity of bread increases by 0.695% d. For every additional 1% of income, the estimated quantity of bread increases by 5.195% O e. For every additional 1% of natural log income, the estimated natural log quantity of bread increases by 0.695%

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

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 3E