File Name: Northeast Weighted Model Results and Implicit Prices.docx

Author: Robert Johnston, Clark University

Created: February 8, 2012

Purpose: Presents results for the weighted Northeast Model and implicit prices.

Northeast Weighted Model

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Start values obtained using MNL model

Dependent variable Choice

Weighting variable W_VAR

Log likelihood function -1031.26047

Estimation based on N = 1011, K = 6

Information Criteria: Normalization=1/N

Normalized Unnormalized

AIC 2.05195 2074.52093

Fin.Smpl.AIC 2.05203 2074.60460

Bayes IC 2.08114 2104.03311

Hannan Quinn 2.06304 2085.73166

Model estimated: Feb 03, 2012, 09:27:41

R2=1-LogL/LogL* Log-L fncn R-sqrd R2Adj

Constants only must be computed directly

Use NLOGIT ;...; RHS=ONE$

Response data are given as ind. choices

Number of obs.= 1011, skipped 0 obs

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Variable| Coefficient Standard Error b/St.Er. P[|Z|>z]

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NEITHER| -.15100 .18667 -.809 .4186

COM_FISH| .04413* .02375 1.858 .0632

FISH_POP| .00669 .03747 .179 .8582

FISH_SAV| .01054*** .00131 8.053 .0000

AQUATIC| .07674** .03781 2.030 .0424

COST| -.01276*** .00241 -5.288 .0000

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Note: ***, **, * = Significance at 1%, 5%, 10% level.

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Line search does not improve fn. Exiting optimization.

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Random Parameters Logit Model

Dependent variable CHOICE

Weighting variable W_VAR

Log likelihood function -858.87657

Restricted log likelihood -1098.20919

Chi squared [ 21 d.f.] 478.66523

Significance level .00000

McFadden Pseudo R-squared .2179299

Estimation based on N = 1011, K = 21

Information Criteria: Normalization=1/N

Normalized Unnormalized

AIC 1.74061 1759.75314

Fin.Smpl.AIC 1.74153 1760.68742

Bayes IC 1.84278 1863.04574
Hannan Quinn 1.77942 1798.99068

Model estimated: Feb 03, 2012, 09:51:53

Constants only must be computed directly

Use NLOGIT ;...;RHS=ONE$

At start values -1031.2605 .1672******

Response data are given as ind. choices

Replications for simulated probs. = 300

Halton sequences used for simulations

RPL model with panel has 348 groups

Variable number of obs./group =NUM_R

Hessian is not PD. Using BHHH estimator

Number of obs.= 1011, skipped 0 obs

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Variable| Coefficient Standard Error b/St.Er. P[|Z|>z]

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|Random parameters in utility functions

NEITHER| -.19167 .34295 -.559 .5762

COM_FISH| .14850*** .04921 3.018 .0025

FISH_POP| .09356 .07931 1.180 .2381

FISH_SAV| .02712*** .00548 4.947 .0000

AQUATIC| .18299* .09398 1.947 .0515

|Nonrandom parameters in utility functions

COST| -.02017*** .00393 -5.137 .0000

|Diagonal values in Cholesky matrix, L.

NsNEITHE| .03763 .93582 .040 .9679

NsCOM_FI| .13113 .19938 .658 .5107

NsFISH_P| .10232 .48295 .212 .8322

NsFISH_S| .00745 .04004 .186 .8525

NsAQUATI| .00419 .89083 .005 .9962

|Below diagonal values in L matrix. V = L*Lt

COM_:NEI| .11719 .17071 .686 .4924

FISH:NEI| .09682 .29348 .330 .7415

FISH:COM| .11361 .23329 .487 .6263

FIS0:NEI| .04639** .02348 1.976 .0482

FIS0:COM| .01359 .03095 .439 .6606

FISH:FIS| .01921 .05553 .346 .7294

AQUA:NEI| .08853 .30670 .289 .7728

AQUA:COM| .10012 .39231 .255 .7986

AQUA:FIS| .08560 .59709 .143 .8860

AQU0:FIS| -.18543 .34632 -.535 .5924

|Standard deviations of parameter distributions

sdNEITHE| .03763 .93582 .040 .9679

sdCOM_FI| .17587 .14002 1.256 .2091

sdFISH_P| .18098 .21738 .833 .4051

sdFISH_S| .05255*** .01532 3.430 .0006

sdAQUATI| .24411 .43106 .566 .5712

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Note: ***, **, * = Significance at 1%, 5%, 10% level.

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Correlation Matrix for Random Parameters

NEITHER COM_FISH FISH_POP FISH_SAV AQUATIC

+-------------+-------------+-------------+-------------+------------

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NEITHER | 1.00000 .66636 .53500 .88280 .36267

COM_FISH| .66636 1.00000 .82460 .78112 .54748
FISH_POP| .53500 .82460 1.00000 .84140 .64976

FISH_SAV| .88280 .78112 .84140 1.00000 .44682

AQUATIC | .36267 .54748 .64976 .44682 1.00000

+-------------+-------------+-------------+-------------+------------

-+

Northeast Implicit Prices: Weighted Model

The following tables summarize the distribution of each implicit price. The statistical

significance of each implicit price can be determined by looking at the percentiles on the

empirical distribution. For example, all percentiles for commercial fish (wtp_com_mean) are in

the positive domain, so that this implicit price is statistically significant at the 1% level.

Estimated Implicit Prices

(mean) wtp_com_mean (commercial fish populations)

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Percentiles Smallest

1% 1.281273 -1.596552

5% 3.369536 .0006117

10% 4.059304 .081922 Obs 1000

25% 5.572303 .4958421 Sum of Wgt. 1000

50% 7.239184 Mean 7.547309

Largest Std. Dev. 3.012806

75% 9.277072 18.84307

90% 11.07869 23.02804 Variance 9.076997

95% 12.76337 23.70783 Skewness .8984211

99% 16.01752 25.8624 Kurtosis 5.894783

(mean) wtp_pop_mean (all fish populations)

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Percentiles Smallest

1% -4.607937 -8.841649

5% -1.841006 -6.793045

10% -.4060666 -6.690606 Obs 1000

25% 1.727979 -6.688567 Sum of Wgt. 1000

50% 4.552494 Mean 4.746262

Largest Std. Dev. 4.318085

75% 7.413868 20.01002

90% 10.02739 22.50277 Variance 18.64586

95% 11.62003 23.16116 Skewness .4483823

99% 16.04164 25.79933 Kurtosis 4.115611
(mean) wtp_sav_mean (fish saved)

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Percentiles Smallest

1% .6451215 .2027048

5% .8465244 .4709032

10% .9505142 .5096945 Obs 1000

25% 1.153097 .5561329 Sum of Wgt. 1000

50% 1.366598 Mean 1.399283

Largest Std. Dev. .3839941

75% 1.607942 2.888671

90% 1.839017 3.105261 Variance .1474514

95% 2.076759 3.194698 Skewness 1.127701

99% 2.547523 4.562718 Kurtosis 8.463369

(mean) wtp_aquat_mean(aquatic ecological condition)

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Percentiles Smallest

1% -1.437585 -3.613533

5% 1.353207 -3.474618

10% 3.09235 -3.194159 Obs 1000

25% 5.686873 -3.146312 Sum of Wgt. 1000

50% 9.199791 Mean 9.344217

Largest Std. Dev. 5.03011

75% 12.77587 22.68429

90% 15.95918 22.93465 Variance 25.30201

95% 18.13723 23.71299 Skewness .2390789

99% 21.4787 31.3448 Kurtosis 3.022245

(mean) wtp_ne_mean (ASC for status quo - no policy)

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Percentiles Smallest

1% -47.00893 -69.01016

5% -34.52572 -60.62623

10% -29.94979 -59.34459 Obs 1000

25% -20.58036 -51.55547 Sum of Wgt. 1000

50% -9.438338 Mean -8.610077

Largest Std. Dev. 17.69803

75% 2.799084 49.61007

90% 13.76251 49.83404 Variance 313.2203

95% 20.23096 68.5407 Skewness .3818289

99% 38.6841 78.97453 Kurtosis 3.920392