Question

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.

          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.

Added by Christopher S.

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