From michael.burton at uwa.edu.au Mon Jan 17 15:08:31 2022 From: michael.burton at uwa.edu.au (Michael Burton) Date: Mon, 17 Jan 2022 04:08:31 +0000 Subject: [Limdep Nlogit List] best/worst in NLOGIT6 Message-ID: I am wondering how the ?worst? choices are dealt with in estimation. Is the ?reverse? logit applied to the worst choices, so the error term is multiplied by -1 and the best and worst choice specification is strictly consistent, or is the more usual ?stacked? approach taken, where marginal utilities of the ?worst? are flipped, but not the error term? Thanks Michael Sent from Mail for Windows From teagle at tceagle.com Tue Jan 18 04:55:08 2022 From: teagle at tceagle.com (Thomas Eagle) Date: Mon, 17 Jan 2022 17:55:08 +0000 Subject: [Limdep Nlogit List] best/worst in NLOGIT6 In-Reply-To: References: Message-ID: <913d1861589c477aaeec50eb644082fb@tceagle.com> There are several ways to do this. You should look at Louviere's book on Best-Worst modeling to see some examples. The MaxDiff approach is to generate tasks for the best choice and generate tasks for the worst choice. You use dummy or effects coding for the items in the tasks. The best tasks are all coded normally. The worst tasks have all their dummy or effects coded values multiplied by -1. If you wish to use the best worst coding then the best alternative is dropped from the generated worst tasks. Tom Eagle -----Original Message----- From: limdep-bounces at mailman.sydney.edu.au On Behalf Of Michael Burton Sent: Sunday, January 16, 2022 8:09 PM To: limdep at limdep.itls.usyd.edu.au Subject: [Limdep Nlogit List] best/worst in NLOGIT6 I am wondering how the 'worst' choices are dealt with in estimation. Is the 'reverse' logit applied to the worst choices, so the error term is multiplied by -1 and the best and worst choice specification is strictly consistent, or is the more usual 'stacked' approach taken, where marginal utilities of the 'worst' are flipped, but not the error term? Thanks Michael Sent from Mail for Windows _______________________________________________ Limdep site list Limdep at mailman.sydney.edu.au https://protect-au.mimecast.com/s/EWVSCr81nyt8qMyQKi7LYUQ?domain=limdep.itls.usyd.edu.au From teagle at tceagle.com Tue Jan 18 04:55:08 2022 From: teagle at tceagle.com (Thomas Eagle) Date: Mon, 17 Jan 2022 17:55:08 +0000 Subject: [Limdep Nlogit List] best/worst in NLOGIT6 In-Reply-To: References: Message-ID: <913d1861589c477aaeec50eb644082fb@tceagle.com> There are several ways to do this. You should look at Louviere's book on Best-Worst modeling to see some examples. The MaxDiff approach is to generate tasks for the best choice and generate tasks for the worst choice. You use dummy or effects coding for the items in the tasks. The best tasks are all coded normally. The worst tasks have all their dummy or effects coded values multiplied by -1. If you wish to use the best worst coding then the best alternative is dropped from the generated worst tasks. Tom Eagle -----Original Message----- From: limdep-bounces at mailman.sydney.edu.au On Behalf Of Michael Burton Sent: Sunday, January 16, 2022 8:09 PM To: limdep at limdep.itls.usyd.edu.au Subject: [Limdep Nlogit List] best/worst in NLOGIT6 I am wondering how the 'worst' choices are dealt with in estimation. Is the 'reverse' logit applied to the worst choices, so the error term is multiplied by -1 and the best and worst choice specification is strictly consistent, or is the more usual 'stacked' approach taken, where marginal utilities of the 'worst' are flipped, but not the error term? Thanks Michael Sent from Mail for Windows _______________________________________________ Limdep site list Limdep at mailman.sydney.edu.au https://protect-au.mimecast.com/s/1DgNCwV1vMfGj65lquVM8FG?domain=limdep.itls.usyd.edu.au From michael.burton at uwa.edu.au Tue Jan 18 12:28:52 2022 From: michael.burton at uwa.edu.au (Michael Burton) Date: Tue, 18 Jan 2022 01:28:52 +0000 Subject: [Limdep Nlogit List] best/worst in NLOGIT6 In-Reply-To: <913d1861589c477aaeec50eb644082fb@tceagle.com> References: <913d1861589c477aaeec50eb644082fb@tceagle.com> Message-ID: Tom That's not my problem. If you estimate the 'stacked' model (Louviere et al p36-37) you are estimating a model that internally inconsistent, by construction. That's because the gumbel error term isn't symmetric. Where that's recognised (e.g Flynn et al, 2008, BMC Medical Research Methodology) its not seen as a big enough issue to prevent the approach being used. Delle Site et al (2019) set out the theory (DOI: 10.1016/j.trb.2019.07.014) and a reading of that paper suggests the Nlogit6 can estimate it correctly (i.e. that the probabilities for the worst choices are defined by a 'reverse' logit). So the purpose of my question is to check if the best/worst command in Nlogit6 actually does that. Michael -----Original Message----- From: limdep-bounces at mailman.sydney.edu.au On Behalf Of Thomas Eagle Sent: Tuesday, 18 January 2022 1:55 AM To: Limdep and Nlogit Mailing List ; limdep at limdep.itls.usyd.edu.au Subject: Re: [Limdep Nlogit List] best/worst in NLOGIT6 There are several ways to do this. You should look at Louviere's book on Best-Worst modeling to see some examples. The MaxDiff approach is to generate tasks for the best choice and generate tasks for the worst choice. You use dummy or effects coding for the items in the tasks. The best tasks are all coded normally. The worst tasks have all their dummy or effects coded values multiplied by -1. If you wish to use the best worst coding then the best alternative is dropped from the generated worst tasks. Tom Eagle -----Original Message----- From: limdep-bounces at mailman.sydney.edu.au On Behalf Of Michael Burton Sent: Sunday, January 16, 2022 8:09 PM To: limdep at limdep.itls.usyd.edu.au Subject: [Limdep Nlogit List] best/worst in NLOGIT6 I am wondering how the 'worst' choices are dealt with in estimation. Is the 'reverse' logit applied to the worst choices, so the error term is multiplied by -1 and the best and worst choice specification is strictly consistent, or is the more usual 'stacked' approach taken, where marginal utilities of the 'worst' are flipped, but not the error term? Thanks Michael Sent from Mail for Windows _______________________________________________ Limdep site list Limdep at mailman.sydney.edu.au https://protect-au.mimecast.com/s/4L7PCP7LAXfKwxLgxU0ASsg?domain=aus01.safelinks.protection.outlook.com _______________________________________________ Limdep site list Limdep at mailman.sydney.edu.au https://protect-au.mimecast.com/s/4L7PCP7LAXfKwxLgxU0ASsg?domain=aus01.safelinks.protection.outlook.com From michael.burton at uwa.edu.au Tue Jan 18 12:28:52 2022 From: michael.burton at uwa.edu.au (Michael Burton) Date: Tue, 18 Jan 2022 01:28:52 +0000 Subject: [Limdep Nlogit List] best/worst in NLOGIT6 In-Reply-To: <913d1861589c477aaeec50eb644082fb@tceagle.com> References: <913d1861589c477aaeec50eb644082fb@tceagle.com> Message-ID: Tom That's not my problem. If you estimate the 'stacked' model (Louviere et al p36-37) you are estimating a model that internally inconsistent, by construction. That's because the gumbel error term isn't symmetric. Where that's recognised (e.g Flynn et al, 2008, BMC Medical Research Methodology) its not seen as a big enough issue to prevent the approach being used. Delle Site et al (2019) set out the theory (DOI: 10.1016/j.trb.2019.07.014) and a reading of that paper suggests the Nlogit6 can estimate it correctly (i.e. that the probabilities for the worst choices are defined by a 'reverse' logit). So the purpose of my question is to check if the best/worst command in Nlogit6 actually does that. Michael -----Original Message----- From: limdep-bounces at mailman.sydney.edu.au On Behalf Of Thomas Eagle Sent: Tuesday, 18 January 2022 1:55 AM To: Limdep and Nlogit Mailing List ; limdep at limdep.itls.usyd.edu.au Subject: Re: [Limdep Nlogit List] best/worst in NLOGIT6 There are several ways to do this. You should look at Louviere's book on Best-Worst modeling to see some examples. The MaxDiff approach is to generate tasks for the best choice and generate tasks for the worst choice. You use dummy or effects coding for the items in the tasks. The best tasks are all coded normally. The worst tasks have all their dummy or effects coded values multiplied by -1. If you wish to use the best worst coding then the best alternative is dropped from the generated worst tasks. Tom Eagle -----Original Message----- From: limdep-bounces at mailman.sydney.edu.au On Behalf Of Michael Burton Sent: Sunday, January 16, 2022 8:09 PM To: limdep at limdep.itls.usyd.edu.au Subject: [Limdep Nlogit List] best/worst in NLOGIT6 I am wondering how the 'worst' choices are dealt with in estimation. Is the 'reverse' logit applied to the worst choices, so the error term is multiplied by -1 and the best and worst choice specification is strictly consistent, or is the more usual 'stacked' approach taken, where marginal utilities of the 'worst' are flipped, but not the error term? Thanks Michael Sent from Mail for Windows _______________________________________________ Limdep site list Limdep at mailman.sydney.edu.au https://protect-au.mimecast.com/s/sGKQC2xMQzipYwD9Kc1T8OA?domain=aus01.safelinks.protection.outlook.com _______________________________________________ Limdep site list Limdep at mailman.sydney.edu.au https://protect-au.mimecast.com/s/sGKQC2xMQzipYwD9Kc1T8OA?domain=aus01.safelinks.protection.outlook.com From teagle at tceagle.com Tue Jan 18 15:39:08 2022 From: teagle at tceagle.com (Thomas Eagle) Date: Tue, 18 Jan 2022 04:39:08 +0000 Subject: [Limdep Nlogit List] best/worst in NLOGIT6 In-Reply-To: References: <913d1861589c477aaeec50eb644082fb@tceagle.com> Message-ID: <4f36ec434a004e66bf21c54009c501da@tceagle.com> I think you'll find NLOGIT estimates it as I described as the best-worst option... but Bill Greene can explain it better than me. That is why I referred you to Louviere, Flynn, and Marley (2015) Best-Worst Scaling: Theory, Methods, and Applications. They lay out in more detail what Flynn, Louviere, et al wrote in 2008. Just trying to help... Tom -----Original Message----- From: limdep-bounces at mailman.sydney.edu.au On Behalf Of Michael Burton Sent: Monday, January 17, 2022 5:29 PM To: Limdep and Nlogit Mailing List ; limdep at limdep.itls.usyd.edu.au Subject: Re: [Limdep Nlogit List] best/worst in NLOGIT6 Tom That's not my problem. If you estimate the 'stacked' model (Louviere et al p36-37) you are estimating a model that internally inconsistent, by construction. That's because the gumbel error term isn't symmetric. Where that's recognised (e.g Flynn et al, 2008, BMC Medical Research Methodology) its not seen as a big enough issue to prevent the approach being used. Delle Site et al (2019) set out the theory (DOI: 10.1016/j.trb.2019.07.014) and a reading of that paper suggests the Nlogit6 can estimate it correctly (i.e. that the probabilities for the worst choices are defined by a 'reverse' logit). So the purpose of my question is to check if the best/worst command in Nlogit6 actually does that. Michael -----Original Message----- From: limdep-bounces at mailman.sydney.edu.au

> On Behalf Of Thomas Eagle Sent: Tuesday, 18 January 2022 1:55 AM To: Limdep and Nlogit Mailing List >; limdep at limdep.itls.usyd.edu.au Subject: Re: [Limdep Nlogit List] best/worst in NLOGIT6 There are several ways to do this. You should look at Louviere's book on Best-Worst modeling to see some examples. The MaxDiff approach is to generate tasks for the best choice and generate tasks for the worst choice. You use dummy or effects coding for the items in the tasks. The best tasks are all coded normally. The worst tasks have all their dummy or effects coded values multiplied by -1. If you wish to use the best worst coding then the best alternative is dropped from the generated worst tasks. Tom Eagle -----Original Message----- From: limdep-bounces at mailman.sydney.edu.au

> On Behalf Of Michael Burton Sent: Sunday, January 16, 2022 8:09 PM To: limdep at limdep.itls.usyd.edu.au Subject: [Limdep Nlogit List] best/worst in NLOGIT6 I am wondering how the 'worst' choices are dealt with in estimation. Is the 'reverse' logit applied to the worst choices, so the error term is multiplied by -1 and the best and worst choice specification is strictly consistent, or is the more usual 'stacked' approach taken, where marginal utilities of the 'worst' are flipped, but not the error term? Thanks Michael Sent from Mail for Windows _______________________________________________ Limdep site list Limdep at mailman.sydney.edu.au https://protect-au.mimecast.com/s/uttaCMwGxOtqrKZN3Tke0e5?domain=aus01.safelinks.protection.outlook.com _______________________________________________ Limdep site list Limdep at mailman.sydney.edu.au https://protect-au.mimecast.com/s/uttaCMwGxOtqrKZN3Tke0e5?domain=aus01.safelinks.protection.outlook.com _______________________________________________ Limdep site list Limdep at mailman.sydney.edu.au https://protect-au.mimecast.com/s/AVWUCNLJyQU0WKB43Fjm8k1?domain=limdep.itls.usyd.edu.au From annika.tienhaara at luke.fi Wed Jan 26 00:38:00 2022 From: annika.tienhaara at luke.fi (Tienhaara Annika (LUKE)) Date: Tue, 25 Jan 2022 13:38:00 +0000 Subject: [Limdep Nlogit List] Log normally distributed cost parameter Message-ID: Dear all, We are estimating random parameters (mixed) logit models with environmental attributes following a normal distribution and the cost parameter lognormal distribution. How can we calculate the means and standard errors of the marginal WTPs for the attributes using Nlogit, based on the unconditional estimates? We have looked into simulating the WTP values for the population, but have not found instructions or examples on how to do this when the cost is lognormal. Estimation in WTP space is not possible, as there we have problems with model convergence. Here is our model and results: |-> NLOGIT;Lhs=CHOICE ;Choices=BAU,ALT1,ALT2 ;Halton ;rpl ;pts=2000 ;pds=6 ;Fcn=a2(n),a3(n),b1(n),b2(n),b3(n),b4(n),b5(n),b6(n),b7(n),b8(n),b9(n),b10(n),b11(l) ;parameters ;Model: U(BAU)=b1*p30+b2*p60+b3*e2+b4*e3+b5*k4+b6*k5+b7*i10+b8*i30+b9*v70+b10*v80+b11*mpc/ U(ALT1)=a2+b1*p30+b2*p60+b3*e2+b4*e3+b5*k4+b6*k5+b7*i10+b8*i30+b9*v70+b10*v80+b11*mpc/ U(ALT2)=a3+b1*p30+b2*p60+b3*e2+b4*e3+b5*k4+b6*k5+b7*i10+b8*i30+b9*v70+b10*v80+b11*mpc$ --------------------------------------------------------------------------- Random Parameters Multinom. Logit Model Dependent variable CHOICE Log likelihood function -2462.16944 Restricted log likelihood -3401.30365 Chi squared [ 26](P= .000) 1878.26841 Significance level .00000 McFadden Pseudo R-squared .2761101 Estimation based on N = 3096, K = 26 Inf.Cr.AIC = 4976.3 AIC/N = 1.607 --------------------------------------- Log likelihood R-sqrd R2Adj No coefficients -3401.3036 .2761 .2731 Constants only -3228.5985 .2374 .2342 At start values -3003.1598 .1801 .1767 Note: R-sqrd = 1 - logL/Logl(constants) --------------------------------------- Response data are given as ind. choices Replications for simulated probs. =2000 Used Halton sequences in simulations. RPL model with panel has 516 groups Fixed number of obsrvs./group= 6 BHHH estimator used for asymp. variance Number of obs.= 3096, skipped 0 obs --------+------------------------------------------------------------------ | Standard Prob. 95% Confidence CHOICE| Coefficient Error z |z|>Z* Interval --------+------------------------------------------------------------------ |Random parameters in utility functions.............................. A2| 2.13805*** .38031 5.62 .0000 1.39265 2.88344 A3| 1.34720*** .36737 3.67 .0002 .62718 2.06723 B1| .70033*** .16475 4.25 .0000 .37743 1.02323 B2| .73475*** .16741 4.39 .0000 .40663 1.06288 B3| .20620 .15004 1.37 .1694 -.08788 .50028 B4| .73427*** .16651 4.41 .0000 .40792 1.06062 B5| -.19350 .14221 -1.36 .1736 -.47223 .08523 B6| .08965 .13512 .66 .5070 -.17519 .35448 B7| .69951*** .17191 4.07 .0000 .36256 1.03645 B8| .64813*** .17595 3.68 .0002 .30329 .99298 B9| .31783* .16785 1.89 .0583 -.01116 .64681 B10| .68545*** .17767 3.86 .0001 .33723 1.03367 B11| -4.10386*** .14879 -27.58 .0000 -4.39548 -3.81224 |Distns. of RPs. Std.Devs or limits of triangular.................... NsA2| .80219*** .22657 3.54 .0004 .35811 1.24627 NsA3| 1.29128*** .18269 7.07 .0000 .93322 1.64934 NsB1| 1.18243*** .19424 6.09 .0000 .80172 1.56314 NsB2| 1.16108*** .25288 4.59 .0000 .66544 1.65672 NsB3| .93324*** .23092 4.04 .0001 .48064 1.38584 NsB4| .95523*** .23226 4.11 .0000 .50001 1.41046 NsB5| .51003* .30579 1.67 .0953 -.08931 1.10936 NsB6| .88595*** .20339 4.36 .0000 .48731 1.28458 NsB7| .72713*** .24920 2.92 .0035 .23871 1.21555 NsB8| 1.33430*** .19572 6.82 .0000 .95070 1.71790 NsB9| .93112*** .24078 3.87 .0001 .45919 1.40305 NsB10| 1.01326*** .21938 4.62 .0000 .58328 1.44324 LsB11| 2.10463*** .16179 13.01 .0000 1.78753 2.42173 --------+------------------------------------------------------------------ Thank you in advance! Best regards Annika