ESTIMATION OF EFFICIENT REGRESSION MODELS FOR APPLIED AGRICULTURAL ECONOMICS RESEARCH



Table 3. Parameter estimates, standard error estimates, and statistical significance of parameters of normal

and non-normal error regression models for corn and soybean prices.

_____OLS

______AR(1)_____

SUR-AR(I)

NSUR-AR(I)

NNSUR-AR(I)

Par. Est.

S.E. Est.

Par. Est.

S.E. Est.

Par. Est.

S.E. Est. Par. Est.

S.E. Est. Par. Est. S.E. Est.

θc

0.9446 0.2875**

μ c

— —

Bco

3.0840

0.1423**

3.0860

0.2263**

3.0817**

0.2273

3.1351

0.1600**

3.1722 0.1065**

Bc1

-1.5611

1.3432ns

-1.4552

2.1317ns

-1.4237ns

2.1879

-2.1217

0.5563**

-2.3509 0.3929**

Bc2

-1.0001

2.6509ns

-1.3787

4.1937ns

-1.4244ns

4.3186

— —

σc

0.3490

0.2811

0.2818**

0.0282

0.2821

0.0282**

0.2990 0.0804**

pc

0.5460

0.1185**

0.5347**

0.1111

0.5353

0.1112**

0.5688 0.0987**

θs

0.5677 0.1600**

μ s

15.7161 5.0376**

Bso

5.2544

0.3246**

5.3045

0.4998**

5.2899

0.4291**

5.3262

0.4172**

5.3607 0.3347**

Bs1

11.8443

3.0636**

11.5373

4.7101**

11.6223

4.0533**

11.1429

3.8273**

10.1967 2.8094**

Bs2

-25.4146

6.0462**

-25.0758

9.2701**

-25.1588 7.9850**

-24.1720

7.4968**

-21.4259 5.3032**

σs__

0.7960

0.6587

0.6645

0.0677**

0.6645

0.0677**

0.7369 0.1603**

ps__

0.5132

0.1214**

0.4066 0.1227**

0.4073

0.1232**

0.4484 0.0763**

pcs

0.3598 0.1310**

0.3597

0.1311**

0.4644 0.1158**

MVCLF

33.86

MVCLF

36.98 ]

MVCLF

36.92

MVCLF 52.54

R2

R2c=0.44

R2s=0.28

R2c=0.61 R2s=0.48

R2c=0.61 R2s=0.47 R2c=0.61 R2s=0.47

R2c=0.61R2s=0.47

Notes: MVCLF stands for the maximum value reached by the concentrated log-likelihood function. Par.
Est. and S.E. Est. refer to the parameter and standard error estimates, respectively. The parameter and
standard error estimates corresponding to B
ci and Bsi, and to Bc2 and Bs2 have been divided by 100 and
10000, respectively. * and ** denote statistical significance and the 90 and 95% level, respectively,
according to two-tailed t tests. The R
2’s are calculated by dividing the regression sums of squares (based on
the autocorrelated {AR(1)} predictions) by the total sums of squares, i.e. it are the square of the correlation
coefficients between the AR(1) predictions and the observed corn (c) and soybean (s) prices.

26



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