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 R
2 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



More intriguing information

1. Tax Increment Financing for Optimal Open Space Preservation: an Economic Inquiry
2. A Bayesian approach to analyze regional elasticities
3. Consumer Networks and Firm Reputation: A First Experimental Investigation
4. The technological mediation of mathematics and its learning
5. The name is absent
6. Regulation of the Electricity Industry in Bolivia: Its Impact on Access to the Poor, Prices and Quality
7. The name is absent
8. An Intertemporal Benchmark Model for Turkey’s Current Account
9. FISCAL CONSOLIDATION AND DECENTRALISATION: A TALE OF TWO TIERS
10. The name is absent