Compare your results with the regression outcome of model (i). What is the theory behind these models (i) and (ii)? Provide your answer briefly and precisely. savings = lambda + beta*Income + v - (i) savings = beta*Income + e- (ii) Dependent Variable: SAVINGS Method: Least Squares Date: 05/14/24 Time: 21:30 Sample: 3/01/1962 12/01/2020 Included observations: 236 Variable Coefficient Std. Error t-Statistic Prob. C 291.9716 421.2459 0.693114 0.4889 INCOME 0.057447 0.001994 28.81010 0.0000 R-squared 0.780080 Mean dependent var 8946.297 Adjusted R-squared 0.779140 S.D. dependent var 9653.553 S.E. of regression 4536.761 Akaike info criterion 19.68625 Sum squared resid 4.82E+09 Schwarz criterion 19.71561 Log likelihood -2320.978 Hannan-Quinn criter. 19.69809 F-statistic 830.0217 Durbin-Watson stat 0.782666 Prob(F-statistic) 0.000000
Added by Marie W.
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It also includes an error term (v). This model assumes that the relationship between savings and income is linear and that the error term is normally distributed with a mean of zero. Show more…
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Sri K.
Consider the following regression model ln(Yi) = β0 + β1X1i + β2X2i + ui This model has been estimated by OLS. The Gretl output is below. Model 1: OLS, using observations 1-74 coefficient std. error t-ratio p-value const 10.1440 1.2691 7.9935 0.0000 X1 -3.0147 0.2544 -11.8500 0.0000 X2 0.0763 0.4348 0.1756 0.8611 Mean dependent var 2.3953 S.D. dependent var 0.4929 Sum squared resid 5.944 S.E. of regression 0.28934 R-squared 0.66486 Adjusted R-squared 0.65542 F(2, 71) 70.427 P-value(F) 0 Log-likelihood -11.699 Akaike criterion 29.398 Schwarz criterion 36.31 Hannan-Quinn 32.156 Compute a prediction of Y using the values X1 = 2.64 and X2 = 2.59. Show all your working.
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