[Limdep Nlogit List] Limdep Digest, Vol 179, Issue 1
Kassie, Girma Tesfahun (ICARDA-Morocco)
g.tesfahun at cgiar.org
Fri Apr 22 00:05:11 AEST 2022
Dear All -
Thank you for the digest.
I did not expect the solution for something that was bugging me from such an email. Thank you.
Best -
Girma.
Girma T. Kassie (Ph.D.)
Principal Agricultural Market Economist
ICARDA
Rabat, Morocco
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Today's Topics:
1. Latent Class Nested Logit Models in NLogit (Namatirai Cheure)
2. Estimating IQ score from DNA, new algorithm, research project
(Repla Esen)
3. (no subject) (Sivanandan Balakrishnan)
4. Re: (no subject) (William Greene)
----------------------------------------------------------------------
Message: 1
Date: Tue, 8 Mar 2022 15:04:57 +0200
From: Namatirai Cheure <u19400251 at tuks.co.za>
To: limdep at limdep.itls.usyd.edu.au
Subject: [Limdep Nlogit List] Latent Class Nested Logit Models in
NLogit
Message-ID:
<CAFjdxQxqKhea1WVtAesq_=BUyCzWvNfPiQLyeAGhvLiJL9+0CQ at mail.gmail.com>
Content-Type: text/plain; charset=UTF-8
Hi,
Is it possible to run a Latent Class Nested Logit Model in NLogit? I managed to run the LC-MNL model but I cannot find an LC-NL model example in the reference guide.
Thanks.
Prayers
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Message: 2
Date: Thu, 14 Apr 2022 20:55:44 -0700
From: Repla Esen <esen.repla at gmail.com>
To: limdep at mailman.sydney.edu.au
Subject: [Limdep Nlogit List] Estimating IQ score from DNA, new
algorithm, research project
Message-ID:
<CAD8B-ZGy4VDDSxBVFJZS5u26gj5cfqQjMQyUwWEoYojCFUW7mQ at mail.gmail.com>
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Hello from Silicon Valley,
We recently developed an algorithm to estimate a human?s IQ score, from raw DNA data.
As of now, our error of estimation is around 5%.
It is currently a research project to decrease the error further. Could you please give it a try so help us improve it?:
https://protect-au.mimecast.com/s/DYE-Cq71mwfOLL0PqfXS2dh?domain=cognidna.com
We also appreciate it if you please share this email with your innovator-minded friends and family members.
Thanks,
Alper Nese
------------------------------
Message: 3
Date: Thu, 21 Apr 2022 15:47:23 +0800
From: Sivanandan Balakrishnan <bsivan720711 at gmail.com>
To: Limdep at mailman.sydney.edu.au
Subject: [Limdep Nlogit List] (no subject)
Message-ID:
<CAG9bbjarn3vgAuvEG0kzeWcOA54sgezcPKs5PTDyVQLsbfGFow at mail.gmail.com>
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Hi all,
I have run a mixed logit and received the result as below. Could I know why the result/output value for the coefficient and standard error is big? Can we use this value?
Random Coefficients Logit Model
Dependent variable INJ
Log likelihood function -1594.14495
Restricted log likelihood -1625.09752
Chi squared [ 26 d.f.] 61.90512
Significance level .00009
McFadden Pseudo R-squared .0190466
Estimation based on N = 3675, K = 52
Inf.Cr.AIC = 3292.3 AIC/N = .896
Model estimated: Apr 21, 2022, 09:22:20
Sample is 1 pds and 3675 individuals
LOGIT (Logistic) probability model
--------+---------------------------------------------------------------
--------+-----
| Standard Prob. 95% Confidence
SEV_INJ| Coefficient Error z |z|>Z* Interval
--------+---------------------------------------------------------------
--------+-----
|Means for random parameters
Constant| -33819.0*** 1378.379 -24.54 .0000 -36520.6 -31117.4
FEMALE| 6855.90*** 245.3723 27.94 .0000 6374.98 7336.82
YOUNG| 2063.63*** 186.5024 11.06 .0000 1698.09 2429.17
OLD| 10445.7*** 511.9553 20.40 .0000 9442.3 11449.1
SPEEDB| 290.380 303.9470 .96 .3394 -305.345 886.105
SPEEDC| 8147.07*** 274.6788 29.66 .0000 7608.71 8685.43
SPEEDD| 12300.9*** 247.6893 49.66 .0000 11815.5 12786.4
MORPEAK| -4331.12*** 269.9351 -16.05 .0000 -4860.19 -3802.06
NOTWOR| 13432.5*** 653.0941 20.57 .0000 12152.5 14712.5
NIGHT| 3017.31*** 250.1793 12.06 .0000 2526.97 3507.65
TRN_LFT| -10355.6*** 840.1674 -12.33 .0000 -12002.3 -8708.9
EJECT| 28717.5*** 672.9396 42.67 .0000 27398.6 30036.5
MN_RDS| -10732.7*** 1072.537 -10.01 .0000 -12834.8 -8630.5
FREWYS| -13659.2*** 1081.154 -12.63 .0000 -15778.2 -11540.2
HVY_VEH| -8961.61*** 176.2324 -50.85 .0000 -9307.02 -8616.20
MAJOR| 7504.13*** 320.9807 23.38 .0000 6875.02 8133.24
EXT_UNR| 13355.3*** 257.5061 51.86 .0000 12850.6 13860.0
ONE_OCC| 6235.70*** 229.4908 27.17 .0000 5785.90 6685.49
Y_1995| -4028.68*** 277.5451 -14.52 .0000 -4572.66 -3484.70
FIRE| 16646.3*** 4557.406 3.65 .0003 7713.9 25578.6
RGFRDOPL| 4785.69*** 170.8597 28.01 .0000 4450.82 5120.57
MTRCYCL| 12285.2*** 1702.179 7.22 .0000 8949.0 15621.4
POL_ATT| 19808.7*** 782.7466 25.31 .0000 18274.5 21342.8
CRO_INT| 1244.51*** 201.7431 6.17 .0000 849.10 1639.92
LA_5_6| 7843.43*** 325.5258 24.09 .0000 7205.42 8481.45
NOT_DIV| -2762.58*** 211.3672 -13.07 .0000 -3176.85 -2348.31
|Scale parameters for dists. of random parameters
Constant| 12999.4*** 161.5314 80.48 .0000 12682.8 13316.0
FEMALE| 26061.1*** 269.5765 96.67 .0000 25532.8 26589.5
YOUNG| 18428.1*** 380.2219 48.47 .0000 17682.9 19173.4
OLD| 21890.7*** 646.5608 33.86 .0000 20623.4 23157.9
SPEEDB| 18213.6*** 219.7653 82.88 .0000 17782.9 18644.3
SPEEDC| 2525.14*** 158.5081 15.93 .0000 2214.47 2835.81
SPEEDD| 12468.8*** 253.4224 49.20 .0000 11972.1 12965.5
MORPEAK| 7986.80*** 231.4354 34.51 .0000 7533.20 8440.41
NOTWOR| 11675.6*** 1015.335 11.50 .0000 9685.5 13665.6
NIGHT| 611.061*** 118.0811 5.17 .0000 379.626 842.496
TRN_LFT| 5810.00*** 2192.580 2.65 .0081 1512.62 10107.38
EJECT| 4890.19*** 694.3294 7.04 .0000 3529.33 6251.05
MN_RDS| 9007.20*** 236.0017 38.17 .0000 8544.65 9469.76
FREWYS| 10586.3*** 171.9844 61.55 .0000 10249.2 10923.4
HVY_VEH| 11451.6*** 257.8199 44.42 .0000 10946.3 11957.0
MAJOR| 4861.01*** 317.1533 15.33 .0000 4239.40 5482.62
EXT_UNR| 552.358*** 187.6679 2.94 .0032 184.535 920.180
ONE_OCC| 6713.97*** 205.4028 32.69 .0000 6311.39 7116.55
Y_1995| 10268.1*** 233.1868 44.03 .0000 9811.1 10725.2
FIRE| 5617.58 6884.030 .82 .4145 -7874.87 19110.03
RGFRDOPL| 1899.09*** 276.3930 6.87 .0000 1357.37 2440.81
MTRCYCL| 47743.3*** 2431.720 19.63 .0000 42977.2 52509.4
POL_ATT| 5481.02*** 113.8669 48.14 .0000 5257.85 5704.20
CRO_INT| 4243.85*** 164.7497 25.76 .0000 3920.95 4566.76
LA_5_6| 16291.6*** 353.2489 46.12 .0000 15599.3 16984.0
NOT_DIV| 12290.5*** 254.1291 48.36 .0000 11792.4 12788.5
--------+---------------------------------------------------------------
--------+-----
Note: ***, **, * ==> Significance at 1%, 5%, 10% level.
-----------------------------------------------------------------------------
Best Regards,
.Sivanandan Balakrishnan
------------------------------
Message: 4
Date: Thu, 21 Apr 2022 09:58:04 -0400
From: William Greene <wgreene at stern.nyu.edu>
To: Limdep and Nlogit Mailing List <limdep at mailman.sydney.edu.au>
Subject: Re: [Limdep Nlogit List] (no subject)
Message-ID:
<CAEJPMR6sBtgOAeg+oV=PmkAG7U-izP2ViSa=Oeg3VAsGbQg-xw at mail.gmail.com>
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Sivanandan Balakrishnan.
The results are not useable. Coefficients like that in a choice model indicate that there is something seriously wrong. You might start to find out if it is the RP specification by fitting an MNL without random parameters. Most likely, you probably have far too many random parameters in your model. A good tight specification should have only a small few RPs.
/B. Greene
On Thu, Apr 21, 2022 at 3:47 AM Sivanandan Balakrishnan < bsivan720711 at gmail.com> wrote:
> Hi all,
>
> I have run a mixed logit and received the result as below. Could I
> know why the result/output value for the coefficient and standard
> error is big? Can we use this value?
>
> Random Coefficients Logit Model
> Dependent variable INJ
> Log likelihood function -1594.14495
> Restricted log likelihood -1625.09752
> Chi squared [ 26 d.f.] 61.90512
> Significance level .00009
> McFadden Pseudo R-squared .0190466
> Estimation based on N = 3675, K = 52
> Inf.Cr.AIC = 3292.3 AIC/N = .896
> Model estimated: Apr 21, 2022, 09:22:20
> Sample is 1 pds and 3675 individuals
> LOGIT (Logistic) probability model
>
> --------+-------------------------------------------------------------
> --------+-------
> | Standard Prob. 95% Confidence
> SEV_INJ| Coefficient Error z |z|>Z* Interval
>
> --------+-------------------------------------------------------------
> --------+-------
> |Means for random parameters
> Constant| -33819.0*** 1378.379 -24.54 .0000 -36520.6 -31117.4
> FEMALE| 6855.90*** 245.3723 27.94 .0000 6374.98 7336.82
> YOUNG| 2063.63*** 186.5024 11.06 .0000 1698.09 2429.17
> OLD| 10445.7*** 511.9553 20.40 .0000 9442.3 11449.1
> SPEEDB| 290.380 303.9470 .96 .3394 -305.345 886.105
> SPEEDC| 8147.07*** 274.6788 29.66 .0000 7608.71 8685.43
> SPEEDD| 12300.9*** 247.6893 49.66 .0000 11815.5 12786.4
> MORPEAK| -4331.12*** 269.9351 -16.05 .0000 -4860.19 -3802.06
> NOTWOR| 13432.5*** 653.0941 20.57 .0000 12152.5 14712.5
> NIGHT| 3017.31*** 250.1793 12.06 .0000 2526.97 3507.65
> TRN_LFT| -10355.6*** 840.1674 -12.33 .0000 -12002.3 -8708.9
> EJECT| 28717.5*** 672.9396 42.67 .0000 27398.6 30036.5
> MN_RDS| -10732.7*** 1072.537 -10.01 .0000 -12834.8 -8630.5
> FREWYS| -13659.2*** 1081.154 -12.63 .0000 -15778.2 -11540.2
> HVY_VEH| -8961.61*** 176.2324 -50.85 .0000 -9307.02 -8616.20
> MAJOR| 7504.13*** 320.9807 23.38 .0000 6875.02 8133.24
> EXT_UNR| 13355.3*** 257.5061 51.86 .0000 12850.6 13860.0
> ONE_OCC| 6235.70*** 229.4908 27.17 .0000 5785.90 6685.49
> Y_1995| -4028.68*** 277.5451 -14.52 .0000 -4572.66 -3484.70
> FIRE| 16646.3*** 4557.406 3.65 .0003 7713.9 25578.6
> RGFRDOPL| 4785.69*** 170.8597 28.01 .0000 4450.82 5120.57
> MTRCYCL| 12285.2*** 1702.179 7.22 .0000 8949.0 15621.4
> POL_ATT| 19808.7*** 782.7466 25.31 .0000 18274.5 21342.8
> CRO_INT| 1244.51*** 201.7431 6.17 .0000 849.10 1639.92
> LA_5_6| 7843.43*** 325.5258 24.09 .0000 7205.42 8481.45
> NOT_DIV| -2762.58*** 211.3672 -13.07 .0000 -3176.85 -2348.31
> |Scale parameters for dists. of random parameters
> Constant| 12999.4*** 161.5314 80.48 .0000 12682.8 13316.0
> FEMALE| 26061.1*** 269.5765 96.67 .0000 25532.8 26589.5
> YOUNG| 18428.1*** 380.2219 48.47 .0000 17682.9 19173.4
> OLD| 21890.7*** 646.5608 33.86 .0000 20623.4 23157.9
> SPEEDB| 18213.6*** 219.7653 82.88 .0000 17782.9 18644.3
> SPEEDC| 2525.14*** 158.5081 15.93 .0000 2214.47 2835.81
> SPEEDD| 12468.8*** 253.4224 49.20 .0000 11972.1 12965.5
> MORPEAK| 7986.80*** 231.4354 34.51 .0000 7533.20 8440.41
> NOTWOR| 11675.6*** 1015.335 11.50 .0000 9685.5 13665.6
> NIGHT| 611.061*** 118.0811 5.17 .0000 379.626 842.496
> TRN_LFT| 5810.00*** 2192.580 2.65 .0081 1512.62 10107.38
> EJECT| 4890.19*** 694.3294 7.04 .0000 3529.33 6251.05
> MN_RDS| 9007.20*** 236.0017 38.17 .0000 8544.65 9469.76
> FREWYS| 10586.3*** 171.9844 61.55 .0000 10249.2 10923.4
> HVY_VEH| 11451.6*** 257.8199 44.42 .0000 10946.3 11957.0
> MAJOR| 4861.01*** 317.1533 15.33 .0000 4239.40 5482.62
> EXT_UNR| 552.358*** 187.6679 2.94 .0032 184.535 920.180
> ONE_OCC| 6713.97*** 205.4028 32.69 .0000 6311.39 7116.55
> Y_1995| 10268.1*** 233.1868 44.03 .0000 9811.1 10725.2
> FIRE| 5617.58 6884.030 .82 .4145 -7874.87 19110.03
> RGFRDOPL| 1899.09*** 276.3930 6.87 .0000 1357.37 2440.81
> MTRCYCL| 47743.3*** 2431.720 19.63 .0000 42977.2 52509.4
> POL_ATT| 5481.02*** 113.8669 48.14 .0000 5257.85 5704.20
> CRO_INT| 4243.85*** 164.7497 25.76 .0000 3920.95 4566.76
> LA_5_6| 16291.6*** 353.2489 46.12 .0000 15599.3 16984.0
> NOT_DIV| 12290.5*** 254.1291 48.36 .0000 11792.4 12788.5
>
> --------+-------------------------------------------------------------
> --------+-------
> Note: ***, **, * ==> Significance at 1%, 5%, 10% level.
>
> ----------------------------------------------------------------------
> -------
>
>
> Best Regards,
> .Sivanandan Balakrishnan
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--
William Greene
Department of Economics, emeritus
Stern School of Business, New York University
44 West 4 St.
New York, NY, 10012
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<https://protect-au.mimecast.com/s/KQ87CwV1vMfL008zBC1RIBu?domain=springer.com>
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