From anushiya.thanapalan at hdr.qut.edu.au Tue Oct 12 16:29:55 2021 From: anushiya.thanapalan at hdr.qut.edu.au (Anushiya Thanapalan) Date: Tue, 12 Oct 2021 05:29:55 +0000 Subject: [Limdep Nlogit List] Clarification Message-ID: Hello, I'm using NLOGIT to run a multinomial logit model. The output did not provide the pseudo R squared value. Rather, it gave the following: R-sqrd = 1 - logL/Logl (constants). What does it all mean? Does this imply that I must perform a manual calculation to determine pseudo R squared? I looked through the documentation but couldn't find a suitable answer. Regards, Anushiya From matenglish123 at gmail.com Fri Oct 29 02:25:20 2021 From: matenglish123 at gmail.com (Mat English) Date: Thu, 28 Oct 2021 10:25:20 -0500 Subject: [Limdep Nlogit List] MNL model with binary choices Message-ID: Hello, I'm a novice user of NLOGIT, but have used it successfully in the past. However, this time I'm having troubles modeling with it. I'm working on a traffic data. Most variables are coded as binary 0,1 (PT in the following example), while some other are continuous integer such as AGE. Following is a sample data shown for reference. D1 is there was a delay, D2 means no delay. CASEID D1 D2 DELAY WT1 WT2 PT AGE 1 0 1 1 500 0.0862 0 72 1 1 0 0 500 0.0862 0 72 2 0 1 1 250 0.0431 1 72 2 1 0 0 250 0.0431 1 72 3 0 1 1 700 0.1207 0 46 3 1 0 0 700 0.1207 0 46 4 0 1 0 350 0.0603 1 62 4 1 0 1 350 0.0603 1 62 5 0 1 1 1100 0.1897 0 61 5 1 0 0 1100 0.1897 0 61 sum =1 The model code with weight WT1 and output is as follows RESET; TIMER; READ; NVAR=51; NOBS=25000; FILE="C:\...\FILENAME.csv"; NAMES=1$ Last observation read from data file was 1850 |-> NLOGIT; LHS=DELAY; CHOICES = D1, D2; MODEL: U(TB)= CONST1 + B1*PT + B2*WALK/ U(NTB) = N1*AGE + N2*AUTO ; WTS=WT1$ ----------------------------------------------------------------------------- Discrete choice (multinomial logit) model Dependent variable Choice Weighting variable WT1 Log likelihood function ************** Estimation based on N = 903, K = 5 Inf.Cr.AIC =********* AIC/N = ******** Model estimated: Oct 28, 2021, 09:27:55 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.= 925, skipped 22 obs --------+-------------------------------------------------------------------- | Standard Prob. 95% Confidence BURDEN| Coefficient Error z |z|>Z* Interval --------+-------------------------------------------------------------------- CONST1| -.05596*** .00071 -78.89 .0000 -.05735 -.05457 B1| .36310*** .00072 507.59 .0000 .36170 .36450 B2| 2.51819*** .00099 2534.02 .0000 2.51624 2.52014 N1| .12821*** .00013 991.21 .0000 .12796 .12846 N2| -1.32626*** .00063 -2110.81 .0000 -1.32749 -1.32503 --------+-------------------------------------------------------------------- Note: ***, **, * ==> Significance at 1%, 5%, 10% level. ----------------------------------------------------------------------------- Note that, the LL, R-sqrd and R2Adj are missing in this output but all the independent variables are significant. With the normalized weight i.e. WT2 the output gets the LL but doesn't show R-sqrd and R2Adj and turns the variables to non-significant. ----------------------------------------------------------------------------- Discrete choice (multinomial logit) model Dependent variable Choice Weighting variable WT2 Log likelihood function -.28858 Estimation based on N = 903, K = 5 Inf.Cr.AIC = 10.6 AIC/N = .012 Model estimated: Oct 28, 2021, 09:28:09 R2=1-LogL/LogL* Log-L fncn R-sqrd R2Adj Constants only must be computed directly Use NLOGIT ;...;RHS=ONE$ Chi-squared[ 4] = .02277 Prob [ chi squared > value ] = .99994 Response data are given as ind. choices Number of obs.= 925, skipped 22 obs --------+-------------------------------------------------------------------- | Standard Prob. 95% Confidence BURDEN| Coefficient Error z |z|>Z* Interval --------+-------------------------------------------------------------------- CONST1| -.05596 16.88721 .00 .9974 -33.15427 33.04236 B1| .36309 17.02909 .02 .9830 -33.01330 33.73948 B2| 2.51819 23.65697 .11 .9152 -43.84862 48.88499 N1| .12821 3.07921 .04 .9668 -5.90693 6.16336 N2| -1.32626 14.95749 -.09 .9293 -30.64241 27.98989 --------+-------------------------------------------------------------------- Note: ***, **, * ==> Significance at 1%, 5%, 10% level. ----------------------------------------------------------------------------- I don't understand what's causing this and which is the correct approach. Since, there are only two outcomes that are exhaustive and mutually exclusive, I also tried a binary logit model which gives me a weird output. LOGIT; LHS=DELAY; CHOICES = D1, D2; RHS = ONE,PT,WALK,AGE,AUTO ; WTS=WT1$ ----------------------------------------------------------------------------- Binary Logit Model for Binary Choice Dependent variable BURDEN Weighting variable WT1 Log likelihood function ************** Restricted log likelihood************** Chi squared [ 4 d.f.] .00000 Significance level 1.00000 McFadden Pseudo R-squared .0000000 Estimation based on N = 1850, K = 5 Inf.Cr.AIC =********* AIC/N = ******** Model estimated: Oct 28, 2021, 09:33:51 Corrected for Choice Based Sampling Hosmer-Lemeshow chi-squared = .05435 P-value= 1.00000 with deg.fr. = 8 --------+-------------------------------------------------------------------- | Standard Prob. 95% Confidence BURDEN| Coefficient Error z |z|>Z* Interval --------+-------------------------------------------------------------------- Constant| 0.0 .00042 .00 1.0000 -.82486D-03 .82486D-03 PT| 0.0 .00049 .00 1.0000 -.97015D-03 .97015D-03 WALK| 0.0 .00055 .00 1.0000 -.10809D-02 .10809D-02 EDUC| 0.0 .4255D-06 .00 1.0000 -.83401D-06 .83401D-06 AUTO| 0.0 .00043 .00 1.0000 -.84621D-03 .84621D-03 --------+-------------------------------------------------------------------- Note: nnnnn.D-xx or D+xx => multiply by 10 to -xx or +xx. Note: ***, **, * ==> Significance at 1%, 5%, 10% level. ----------------------------------------------------------------------------- Can you kindly explain which one is the correct modeling approach and which weight should be used. Regards, Matt ReplyForward From khlim2 at gmail.com Sat Oct 30 05:56:38 2021 From: khlim2 at gmail.com (KH Lim) Date: Fri, 29 Oct 2021 13:56:38 -0500 Subject: [Limdep Nlogit List] Censored normal distribution in Mixed Logit and its correlation Message-ID: Hi all, Suppose I estimate a mixed logit model with two variables. Variable A is distributed normal (n), and B is distributed censored right normal (m). My questions are: 1. Is the reported coefficient of B the mean or the mean of B's underlying normal distribution? For example, if the reported coefficient is 0.5, and standard deviation is 2, is the mean of B indeed 0.5? Or is it somewhere more to the right? 2. In the NLOGIT 5 manual, page N-530: "If you will be mixing distributions, the specification of correlated parameters, while allowable, produces ambiguous results. The nature of the correlation is difficult to define. However, the program will have no unusual difficulty estimating a model in which correlated parameters have different distributions. " Is the correlation matrix produced by the estimator subject to such complications in the case of censored normal distribution? If so, can a positive correlation still mean "a person who likes A tends to like B"? Thank you, Kar Lim From wgreene at stern.nyu.edu Sun Oct 31 01:10:38 2021 From: wgreene at stern.nyu.edu (William Greene) Date: Sat, 30 Oct 2021 10:10:38 -0400 Subject: [Limdep Nlogit List] Censored normal distribution in Mixed Logit and its correlation In-Reply-To: References: Message-ID: Dear Kar Lim. First, I'll assume you mean that the coefficients on "A" and "B" are random with normal and censored normal distributions. (1). beta(i) = Min(0,beta+sigma*w(i)) where w(i) ~ N[0,1]. In your example, beta = .5 and sigma = 2. The mean E[beta(i)] is somewhere left of zero. You can use the laws of probability, E[.] = 0*Prob(beta+sigma*w(i)>=0) + Prob(<0) * E[<=0], which is a little involved. The actual value for your example is about -0.6. What is reported is the (beta,sigma). (2) The correlation matrix applies to the underlying primitives. For your example, if the coefficient on A is gamma = .3 + 3*v(i), the correlation reported is that between gamma and (beta+sigma*w(i)). The correlation of the truncated variable will be somewhat less, since after the truncation, there is variation in gamma that is not accompanied by variation in beta_sigma*w. I'm not sure a positive correlation could always mean "a person who likes A tends to like B." For your specific examples, I would say that a positive correlation of the primitives would indeed accompany a positive correlation of gamma with beta. I'm not sure about your qualitative interpretation, however. Regards, Bill Greene On Fri, Oct 29, 2021 at 2:57 PM KH Lim wrote: > Hi all, > > Suppose I estimate a mixed logit model with two variables. Variable A is > distributed normal (n), and B is distributed censored right normal (m). My > questions are: > > 1. Is the reported coefficient of B the mean or the mean of B's underlying > normal distribution? For example, if the reported coefficient is 0.5, and > standard deviation is 2, is the mean of B indeed 0.5? Or is it somewhere > more to the right? > > 2. In the NLOGIT 5 manual, page N-530: > "If you will be mixing distributions, the specification of correlated > parameters, while allowable, produces ambiguous results. The nature of the > correlation is difficult to define. However, the program will have no > unusual difficulty estimating a model in which correlated parameters have > different distributions. " > > Is the correlation matrix produced by the estimator subject to such > complications in the case of censored normal distribution? If so, can a > positive correlation still mean "a person who likes A tends to like B"? > > Thank you, > Kar Lim > _______________________________________________ > Limdep site list > Limdep at mailman.sydney.edu.au > https://protect-au.mimecast.com/s/FzGnCGv0oyC1KMqjqSKZgk6?domain=limdep.itls.usyd.edu.au > > -- William Greene Department of Economics, emeritus Stern School of Business, New York University 44 West 4 St. New York, NY, 10012 URL: https://protect-au.mimecast.com/s/y9WDCJyBrGfq3Yy0yiGYsiA?domain=people.stern.nyu.edu Email: wgreene at stern.nyu.edu Editor in Chief: Journal of Productivity Analysis Editor in Chief: Foundations and Trends in Econometrics Associate Editor: Economics Letters Associate Editor: Journal of Business and Economic Statistics