estimator

Estimates by Non Linear Least Squares

Estimates by robust least squares

Estimates by quantile regression (LAD)

${\beta }_1$

21.17552 (0.625244)

[0.5323]

33.75394 (1.205737)

[0.2279]

33.27137 (1.349164)

[0.1783]

${\beta }_2$

−0.790615 (−11.7466)

[0.0000]

−0.683833 (−12.292)

[0.0000]

−0.688004 (−10.974)

[0.0000]

${\beta }_3$

−0.681340 (−6.40577)

[0.0000]

−0.803635 (−12.2917)

[0.0000]

−0.915199 (−9.639674)

[0.0000]

${\beta }_4$

0.154871 (3.185667)

[0.0016]

0.0838897 (2.087812)

[0.0368]

0.118790 (1.349633)

[0.1782]

${\beta }_5$

0.139797 (3.13336)

[0.0010]

0.168695 (4.837055)

[0.0000]

0.203367 (3.983420)

[0.0001]

Diagnostics

Adjusted R-square

0.501112

0.341754

0.279154

Standard error of regression

401.0800

406.9392

409.2267

Log likelihood

−2191.756

F-statistic

75.07887

p-value of F-statistic

0.000000

Durbin-Watson statistic

1.839722

Schwarz information criterion

14.90528

400.9220

Q (3)

[0.012]

[0.088]

[0.154]

Q (6)

[0.000]

[0.000]

[0.000]

Q (12)

[0.000]

[0.000]

[0.000]

Q² (3)

[0.034]

[0.110]

[0.070]

Q² (6)

[0.068]

[0.199]

[0.120]

Q² (12)

[0.044]

[0.288]

[0.169]

Normality test

[0.000000]

[0.000000]

[0.000000]

Ramsey RESET test

[0.0019]

[0.6113]