ESTIMATION OF EFFICIENT REGRESSION MODELS FOR APPLIED AGRICULTURAL ECONOMICS RESEARCH



Table 5. Parameter estimates, standard error estimates, and statistical significance of parameters of normal and
non-normal error regression models for the West Texas cotton basis.

__________________NHAR(4)

NNHAR(4)

FNHAR(4)

FNNHAR(4)

Par.

Var.

P.E.

S.E.

P.E.

S.E.

P.E.

S.E.

P.E.

S.E.

pi

—

0.9807

0.0687i

0.9626i

0.02720i

0.98904

0.06593i

0.95853

0.02773i

p2

—

-0.2385

0.09652

-0.i3640

0.04i90i

-0.24666

0.09358i

-0.i3685

0.04244i

--

p3

—

0.i3i9

0.0975

0.i34i8

0.045i8i

0.i3667

0.09606

0.i3438

0.04602i

—---

p4

—

-0.i073

0.06i23

-0.i5764

0.03732i

-0.ii249

0.060i73

-0.i5605

0.03682i

--

_B0___

—

-i4.5204

i2.2673

-i3.7478

8.60659

-i8.26522

7.848872

-ii.83432

6.663783

_B__

TXP

0.i930

0.5i59

-0.ii620

0.i3i43

0.i8625

0.46566

-0.i2095

0.i2967

_B__

USP

0.3022

0.2476

0.3i76i

0.i43032

0.330ii

0.23807

0.34039

0.08453i

Вз____

FP

0.09i3

0.i452

0.23745

0.08869i

0.i020i

0.i3689

0.24439

0.06782i

-B__

TXBS

-0.3323

0.3043

-0.477i5

0.258i53

-0.32229

0.29i86

-0.48446

0.245362

_B__

USBS

0.i800

0.3508

0.52388

0.i6576i

0.i8375

0.343i7

0.55098

0.i559ii

_Вб___

FBS

0.0i62

0.i053

0.i6266

0.068242

0.03877

0.i05i5

0.i7332

0.04436i

_B7___

FMU

-0.036i

0.3599

-0.26306

0.ii3202

0.0226i

0.i73i4

-0.26656

0.i0223i

_B__

RRI

0.0i46

0.i479

0.03i85

0.08302

—

—

_B__

STRC

-i.4566

i.2934

-i.i5475

i.26i64

-i.35748

i.252i4

-i.i3765

i.23339

Bio

FDPR

i.0527

0.6739

0.43i63

0.47683

i.03744

0.67i4i

0.35747

0.38805

Bii

SD

-i.099i

0.4225i

-0.69499

0.23655i

-i.06572

0.4i4i02

-0.636i5

0.i8ii5i

Bi2

PDi

2.i954

2.36i4

i.85i66

i.84490

i.29i06

i.i5962

2.38576

i.ii5272

Bi3

PD2

i.i976

2.6599

-0.85989

2.30983

—

—

2
σ

—

i6.0036

2.8967i

ii.35527

4.i3477i

i6.i3068

3.00359i

i0.95924

3.63400i

2

σ SD

SD

-ii.3i79

2.8i22i

-5.82i20

3.8776i

-ii.505i8

2.778i5i

-5.42047

3.343203

2
σ PDi

PDi

-0.39i9

i.3692

-i.76i88

i.42804

-0.38268

i.54083

-i.80659

i.40307

2

σ PD2

PD2

-3.4360

i.0000i

-3.9i090

i.35i65i

-3.35245

i.02i57i

-3.89927

i.3395ii

-Θ__

—

—

—

0.95789

0.225i4i

—

—

0.93976

0.20077i

μ.

—

—

—

0.69869

0.250i7i

—

—

0.72557

0.26i77i

—---

ΘSD

SD

—

—

-i.2i066

0.308iii

—

—

-i.i9269

0.29i55i

Θpdi

PDi

—

—

0.02i53

0.i9ii6

—

—

ΘpD2_

PD2

—

—

0.576i8

0.i9320i

—

—

0.57207

0.i7i90i

MVLLF

-352.820

-282.353

-352.964

-282.448

R2

0.77

0.77

0.77

0.77

Notes: NHAR(4), NNHAR(4), FNHAR(4) and FNNHAR(4) refer to the initial normal and non-normal and to the
final normal and non-normal heteroskedastic fourth-order autoregressive models; the parameters (Par.) and
variables (Var.) are as defined in the text; P.E. and S.E. refers to the parameter and standard error estimates; 1, 2,
and 3 denote statistical significance at the 1%, 5% and 10% level, respectively, according to two-tailed t-tests;
MVLLF refers to the maximum value reached by the model’s concentrated log-likelihood function; and the R2 is
calculated by dividing the regression sum of squares (based on the autocorrelated {AR(4)} predictions) by the
total sum of squares, i.e. it is the square of the correlation coefficient between the AR(4) predictions and the
observed basis values.

28



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