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a) State the estimated OLS regression and interpret the slope coefficients. b) Test at 5% significance level whether or not the coefficient on GNP is significantly less than one. c) Test at 5% significance level whether or not the coefficient on military sales/assistance is significant.

a) State the estimated OLS regression and interpret the slope coefficients. b) Test at 5% significance level whether or not the coefficient on GNP is significantly less than one. c) Test at 5% significance level whether or not the coefficient on military sales/assistance is significant.

Linear Algebra: A Modern Introduction
Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter7: Distance And Approximation
Section7.3: Least Squares Approximation
Problem 29EQ
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a) State the estimated OLS regression and interpret the slope coefficients. b) Test at 5% significance level whether or not the coefficient on GNP is significantly less than one. c) Test at 5% significance level whether or not the coefficient on military sales/assistance is significant.
Transcribed Image Text:a) State the estimated OLS regression and interpret the slope coefficients. b) Test at 5% significance level whether or not the coefficient on GNP is significantly less than one. c) Test at 5% significance level whether or not the coefficient on military sales/assistance is significant.
In order to explain the U.S. defense budget, the following variables are considered. Data from 1962-1981 is collected. Variable Definition Y Defense budget-outlay for year t, $ billions X2 GNP for year t, $ billions X3 U.S. military sales/assistance in year t, $ billions X4 Aerospace industry sales, $ billions Dependent Variable: Y Method: Least Squares Date: 11/22/21 Time: 11:20 Sample: 1962 1981 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. X2 0.016703 0.007017 2.380261 0.0301 X3 -0.696174 0.453978 -1.533497 0.1447 X4 1.467729 0.277608 5.287047 0.0001 22.77514 3.311695 6.877186 0.0000 R-squared 0.971088 Mean dependent var 83.86000 Adjusted R-squared 0.965667 S.D. dependent var 28.97771 S.E. of regression Sum squared resid 5.369339 Akaike info criterion 6.376143 461.2769 Schwarz criterion 6.575290 Log likelihood -59.76143 Hannan-Quinn criter. 6.415019 F-statistic 179.1337 Durbin-Watson stat 0.676777 Prob(F-statistic) 0.000000
Transcribed Image Text:In order to explain the U.S. defense budget, the following variables are considered. Data from 1962-1981 is collected. Variable Definition Y Defense budget-outlay for year t, $ billions X2 GNP for year t, $ billions X3 U.S. military sales/assistance in year t, $ billions X4 Aerospace industry sales, $ billions Dependent Variable: Y Method: Least Squares Date: 11/22/21 Time: 11:20 Sample: 1962 1981 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. X2 0.016703 0.007017 2.380261 0.0301 X3 -0.696174 0.453978 -1.533497 0.1447 X4 1.467729 0.277608 5.287047 0.0001 22.77514 3.311695 6.877186 0.0000 R-squared 0.971088 Mean dependent var 83.86000 Adjusted R-squared 0.965667 S.D. dependent var 28.97771 S.E. of regression Sum squared resid 5.369339 Akaike info criterion 6.376143 461.2769 Schwarz criterion 6.575290 Log likelihood -59.76143 Hannan-Quinn criter. 6.415019 F-statistic 179.1337 Durbin-Watson stat 0.676777 Prob(F-statistic) 0.000000
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