Refer to the following computer output from estimating the parameters of the nonlinear modelY=aRbsc7dThe computer output from the regression analysis is:DEPENDENT VARIABLE: LNY R-SQUARE32 0.7766OBSERVATIONS:VARIABLEINTERCEPTLNRP-VALUE ON F0.0001PARAMETER ESTIMATE STANDARD ERROR T-RATIO-0.6931F-RATIO4.66-0.448.2832.440.321.36-2.173.43-1.83P-VALUE1.800.0390LNS0.24LNT4.60Based on the information in the table, the nonlinear relation can be transformed into the following linear regression model:Multiple Choicein Y= 1n a.ln R.1n S.1n Tin Y= 1na + b1nR+ cins + din T1n Y = 1n(aRb SC7d)Y = 1n(aRb Sc7d)0.00190.07740.0826

Given information: We have given a non-linear model i.e., Y = aRbScTd In order to transform the…

Refer to the following computer output from estimating the parameters of the nonlinear model Y=aRbSCTd The computer output from the regression analysis is: DEPENDENT VARIABLE: LNY R-SQUARE OBSERVATIONS: 32 0.7766 VARIABLE INTERCEPT F-RATIO 32.44 P-VALUE ON F 0.0001 PARAMETER ESTIMATE STANDARD ERROR T-RATIO -2.17 3.43 -0.6931 4.66 -0.44 8.28 P-VALUE 0.0390 0.0019 0.0774 0.0826 0.32 LNR 1.36 LNS 0.24 -1.83 LNT 4.60 1.80 Based on the information in the table, the nonlinear relation can be transformed into the following linear regression model:

Refer to the following computer output from estimating the parameters of the nonlinear model Y=aRbSCTd The computer output from the regression analysis is: DEPENDENT VARIABLE: LNY R-SQUARE OBSERVATIONS: 32 0.7766 VARIABLE INTERCEPT F-RATIO 32.44 P-VALUE ON F 0.0001 PARAMETER ESTIMATE STANDARD ERROR T-RATIO -2.17 3.43 -0.6931 4.66 -0.44 8.28 P-VALUE 0.0390 0.0019 0.0774 0.0826 0.32 LNR 1.36 LNS 0.24 -1.83 LNT 4.60 1.80 Based on the information in the table, the nonlinear relation can be transformed into the following linear regression model:

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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Q1
(e) Suppose you have been given the following ordinary least squares (OLS) regression result. Estimated Long Run Coefficients using the ARDL Approach ARDL (1,2,2,2,0,2) selected based on Akaike Information Criterion Dependent variable is LY 33 observations used for estimation from 1987 to 2019 T-Ratio [Prob.] 4.6671[0.000] 4.6678[0.051] 7.9897[0.043] -4.802[0.009] 2.3898[0.028] 1.0498[0.308] Regressor Coefficient Standard Error 0.36068 0.45447 LK 0.077280 LM 0.097363 0.48751 -0.41208 0.19057 0.52521 LE 0.061017 LF 0.085800 LT 0.079744 C 0.500320 where, Y = Economic growth K = Capital M = Employment E = Electricity consumption F = Foreign direct investment T= Technology (i) Write the regression equation. Interpret the estimated coefficients. (ii) Which explanatory variables are significant at the 1%, 5% and 10% level? Which variables are insignificant? Briefly explain.