From amz at albertomzanni.com Wed Jul 1 00:04:27 2020 From: amz at albertomzanni.com (Alberto M Zanni) Date: Tue, 30 Jun 2020 15:04:27 +0100 Subject: [Limdep Nlogit List] problem exporting results into Excel Message-ID: <7222f047-249d-416e-9f5b-fa6211e57b1d@www.fastmail.com> Dear all, I have been struggling a bit while exporting results to excel (I am using NLogit version 5). This is a simple case my command open; export="C:\Users\abzn\Documents\Alberto\DLP\resultsMNL11.csv" $ nlogit; lhs=choice; choices=A1,A2,A3; rhs=gcov, flower, tcov, isl_mid, isl_wide, bl_div,bl_slow, p_island, lane, tax, sq; export $ The results from the output below are different from those in the Excel table produced by the command above as the confidence interval values are inserted as cofficients of the next variable and so on... I found a previous message reporting this but could not find the reply as well. Am I doing anything wrong? is there a way to resolve this? or do you know of a quicker trick to copy results into excel? thank you very much Alberto --------+-------------------------------------------------------------------- | Standard Prob. 95% Confidence CHOICE| Coefficient Error z |z|>Z* Interval --------+-------------------------------------------------------------------- GCOV| .01168*** .00266 4.40 .0000 .00647 .01688 FLOWER| .10408 .14518 .72 .4734 -.18047 .38862 TCOV| .00444 .00274 1.62 .1054 -.00093 .00981 ISL_MID| -.28088* .16282 -1.73 .0845 -.59999 .03824 ISL_WIDE| -.17648 .15446 -1.14 .2532 -.47921 .12625 BL_DIV| .25986** .12975 2.00 .0452 .00555 .51417 BL_SLOW| .01951 .12755 .15 .8785 -.23049 .26950 P_ISLAND| .11702 .09473 1.24 .2167 -.06866 .30270 LANE| -.12308** .05929 -2.08 .0379 -.23929 -.00687 TAX| -.00014*** .2962D-04 -4.69 .0000 -.00020 -.00008 SQ| -1.69585*** .31846 -5.33 .0000 -2.32002 -1.07169 --------+-------------------------------------------------------------------- Note: nnnnn.D-xx or D+xx => multiply by 10 to -xx or +xx. Note: ***, **, * ==> Significance at 1%, 5%, 10% level. ----------------------------------------------------------------------------- Last Model Estimation Results Variable Coeff. Std.Err. t-ratio P-value GCOV 1.17E-02 2.66E-03 4.39608 1.10E-05 FLOWER 6.47E-03 1.69E-02 0.104077 0.145178 TCOV 0.716888 0.473443 -0.18047 0.388621 ISL_MID 4.44E-03 2.74E-03 1.61915 0.105416 ISL_WIDE -9.34E-04 9.81E-03 -0.28088 0.162818 BL_DIV -1.72509 8.45E-02 -0.59999 3.82E-02 BL_SLOW -0.17648 0.154456 -1.14258 0.253214 P_ISLAND -0.47921 0.12625 0.25986 0.129754 LANE 2.00271 4.52E-02 5.55E-03 0.514174 TAX 1.95E-02 0.127552 0.152926 0.878457 SQ -0.23049 0.269504 0.117019 9.47E-02 From Thao.T.Thai at monash.edu Thu Jul 2 13:32:24 2020 From: Thao.T.Thai at monash.edu (Thao Thai) Date: Thu, 2 Jul 2020 13:32:24 +1000 Subject: [Limdep Nlogit List] MX logit using different algorithms Message-ID: Hi Nlogit users, I am running a Mixed logit model with different algorithms which produce exactly the same set of results. - Newton-Raphson algorithm: I got this message "Line search at iteration 80 does not improve the function. Exiting optimization" - BHHH algorithm: The model can converge after 80 iterations. - BFGS algorithm: The model can converge after 81 iterations Can I use the results given that three algorithms produced the same coefficient estimates? Why do all three algorithms produce the same results with different messages? Thank you so much for your help so far. I really appreciate it. Best regards, Thao From wgreene at stern.nyu.edu Fri Jul 3 06:27:13 2020 From: wgreene at stern.nyu.edu (William Greene) Date: Thu, 2 Jul 2020 16:27:13 -0400 Subject: [Limdep Nlogit List] MX logit using different algorithms In-Reply-To: References: Message-ID: Thao. The mixed logit defaults to BFGS, which is the best algorithm for that class of models. The diagnostic in each case corresponds to the request that you issued, but you should be receiving only the BFGS results. You can check this by adding ;Output=3 to the commands. /Bill Greene On Wed, Jul 1, 2020 at 11:33 PM Thao Thai via Limdep < limdep at mailman.sydney.edu.au> wrote: > Hi Nlogit users, > > I am running a Mixed logit model with different algorithms which produce > exactly the same set of results. > > - Newton-Raphson algorithm: I got this message "Line search at iteration 80 > does not improve the function. Exiting optimization" > > - BHHH algorithm: The model can converge after 80 iterations. > > - BFGS algorithm: The model can converge after 81 iterations > > Can I use the results given that three algorithms produced the same > coefficient estimates? Why do all three algorithms produce the same > results with different messages? > > Thank you so much for your help so far. I really appreciate it. > Best regards, > Thao > _______________________________________________ > Limdep site list > Limdep at mailman.sydney.edu.au > http://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/_wKdC81V0PTLKw64tnt5R4?domain=people.stern.nyu.edu Email: wgreene at stern.nyu.edu Ph. +1.646.596.3296 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 From Thao.T.Thai at monash.edu Sat Jul 4 12:29:42 2020 From: Thao.T.Thai at monash.edu (Thao Thai) Date: Sat, 4 Jul 2020 12:29:42 +1000 Subject: [Limdep Nlogit List] Simulation using estimates from SP data and ASCs from RP data Message-ID: Hi Nlogit users, I am simulating some scenarios using the coefficient estimates from a CL model on SP data and alternative specific constants (ASCs) calibrated to reflect the RP data. However, the reported market shares in the base case shows the market shares observed in SP data. The reported market shares of the simulated scenarios are also compared to these SP data market shares, which I think is not correct (It should be compared to the market shares from RP data)? Could you please kindly let me know if my syntax below is correct? Thank you so much for your help! Thao |-> sample;all$ |-> reject;sprp=1$ |-> Nlogit ;lhs = cho, cset, alti ;choices = H, C, P, I, G, N ;crosstabs ;checkdata ;model: U(H) = rl_h1 * RL_H1 + rl_h2 * RL_H2 + fl_ * FL_H + cr_h1 * CR_H1 + cr_2 * CR_H2 + lo_h1 * LO_H1 + sa_ * SA_H / U(C) = com + rl_c1 * RL_C1 + rl_c2 * RL_C2 + fl_ * FL_C + cr_c1 * CR_C1 + cr_2 * CR_C2 + lo_c1 * LO_C1 + lo_c2 * LO_C2 + sa_ * SA_C/ U(P) = pri + rl_p1 * RL_P1 + fl_ * FL_P + cr_p1 * CR_P1 + cr_2 * CR_P2 + lo_p1 * LO_P1 + lo_p2 * LO_P2 + sa_ * SA_P/ U(I) = ind + rl_i1 * RL_I1 + rl_i2 * RL_I2 + fl_ * FL_I + cr_i1 * CR_I1 + lo_i1 * LO_I1 + sa_ * SA_I/ U(G) = gov + rl_g1 * RL_G1 + fl_ * FL_G + cr_g1 * CR_G1 + lo_g1 * LO_G1 + sa_ * SA_G/ U(N) = non + rl_n1 * RL_N1 + fl_ * FL_N + cr_n1 * CR_N1 + lo_n1 * LO_N1 + lo_n2 * LO_N2 + sa_ * SA_N $ +----------------------------------------------------------+ | Inspecting the data set before estimation. | | These errors mark observations which will be skipped. | | Row Individual = 1st row then group number of data block | +----------------------------------------------------------+ No bad observations were found in the sample Iterative procedure has converged Normal exit: 5 iterations. Status=0, F= .4001834D+04 ----------------------------------------------------------------------------- Discrete choice (multinomial logit) model Dependent variable Choice Log likelihood function -4001.83388 Estimation based on N = 2434, K = 32 Inf.Cr.AIC = 8067.7 AIC/N = 3.315 --------------------------------------- Log likelihood R-sqrd R2Adj ASCs only model must be fit separately Use NLOGIT ;...;RHS=ONE$ Note: R-sqrd = 1 - logL/Logl(constants) --------------------------------------- Chi-squared[27] = 655.81735 Prob [ chi squared > value ] = .00000 Response data are given as ind. choices Number of obs.= 2434, skipped 0 obs --------+-------------------------------------------------------------------- | Standard Prob. 95% Confidence CHO| Coefficient Error z |z|>Z* Interval --------+-------------------------------------------------------------------- RL_H1| .07005 .16175 .43 .6650 -.24697 .38707 RL_H2| .23104* .13433 1.72 .0855 -.03225 .49433 FL_| .17867*** .05585 3.20 .0014 .06920 .28813 CR_H1| .27104** .13131 2.06 .0390 .01368 .52840 CR_2| .05296 .09477 .56 .5763 -.13279 .23871 LO_H1| -.41466*** .12119 -3.42 .0006 -.65219 -.17713 SA_| .01231*** .00086 14.30 .0000 .01062 .01399 COM| -.10806 .15871 -.68 .4959 -.41913 .20300 RL_C1| .38787** .16751 2.32 .0206 .05956 .71618 RL_C2| .26364* .15216 1.73 .0832 -.03459 .56187 CR_C1| .19661 .13691 1.44 .1510 -.07174 .46496 LO_C1| -.22246* .12854 -1.73 .0835 -.47440 .02949 LO_C2| -.82061*** .15800 -5.19 .0000 -1.13027 -.51094 PRI| .40590*** .14002 2.90 .0037 .13146 .68034 RL_P1| .01672 .12447 .13 .8932 -.22725 .26068 CR_P1| .33357** .13132 2.54 .0111 .07619 .59095 LO_P1| -.95156*** .14221 -6.69 .0000 -1.23029 -.67282 LO_P2| -1.03276*** .14087 -7.33 .0000 -1.30885 -.75667 IND| -1.11685*** .15286 -7.31 .0000 -1.41645 -.81725 RL_I1| .64312*** .14665 4.39 .0000 .35570 .93054 RL_I2| .79032*** .15550 5.08 .0000 .48555 1.09508 CR_I1| .61024*** .12199 5.00 .0000 .37115 .84933 LO_I1| -.64615*** .12354 -5.23 .0000 -.88828 -.40402 GOV| -.07470 .14043 -.53 .5948 -.34993 .20053 RL_G1| -.33174** .12927 -2.57 .0103 -.58510 -.07838 CR_G1| .51684*** .12294 4.20 .0000 .27588 .75781 LO_G1| -.57783*** .12464 -4.64 .0000 -.82212 -.33354 NON| -.26651* .14521 -1.84 .0665 -.55112 .01810 RL_N1| -.08143 .13884 -.59 .5576 -.35355 .19070 CR_N1| .36292*** .13942 2.60 .0092 .08965 .63618 LO_N1| -.58506*** .15904 -3.68 .0002 -.89677 -.27336 LO_N2| -.46639*** .16171 -2.88 .0039 -.78333 -.14945 --------+-------------------------------------------------------------------- ***, **, * ==> Significance at 1%, 5%, 10% level. Model was estimated on Jul 04, 2020 at 00:17:29 PM ----------------------------------------------------------------------------- |-> sample;all$ |-> reject;sprp=1$ |-> Nlogit ;lhs = cho, cset, alti ;choices = H, C, P, I, G, N/0.23,0.53,0.04,0.04,0.09,0.07 ;checkdata ;show 'Alg=BFGS ;calibrate ; simulation ; scenario: RL_C1(c)=1/RL_C2(c)=0 ;model: U(H) = rl_h1[0.0700481] * RL_H1 + rl_h2[0.231038] * RL_H2 + fl_[0.178665] * FL_H + cr_h1[0.271042] * CR_H1 + cr_2[0.0529584] * CR_H2 + lo_h1[-0.414659] * LO_H1 + sa_[0.0123069] * SA_H / U(C) = com + rl_c1[0.387869] * RL_C1 + rl_c2[0.263637] * RL_C2 + fl_[0.178665] * FL_C + cr_c1[0.19661] * CR_C1 + cr_2[0.0529584] * CR_C2 + lo_c1[-0.222456] * LO_C1 + lo_c2[-0.820609] * LO_C2 + sa_[0.0123069] * SA_C / U(P) = pri + rl_p1[0.0167174] * RL_P1 + fl_[0.178665] * FL_P + cr_p1[0.333571] * CR_P1 + cr_2[0.0529584] * CR_P2 + lo_p1[-0.951557] * LO_P1 + lo_p2[-1.03276] * LO_P2 + sa_[0.0123069] * SA_P / U(I) = ind + rl_i1[0.643119] * RL_I1 + rl_i2[0.790318] * RL_I2 + fl_[0.178665] * FL_I + cr_i1[0.610239] * CR_I1 + lo_i1[-0.64615] * LO_I1 + sa_[0.0123069] * SA_I / U(G) = gov + rl_g1[-0.331743] * RL_G1 + fl_[0.178665] * FL_G + cr_g1[0.516844] * CR_G1 + lo_g1[-0.57783] * LO_G1 + sa_[0.0123069] * SA_G / U(N) = non + rl_n1[-0.0814267] * RL_N1 + fl_[0.178665] * FL_N + cr_n1[0.36292] * CR_N1 + lo_n1[-0.585064] * LO_N1 + lo_n2[-0.466387] * LO_N2 + sa_[0.0123069] * SA_N$ +----------------------------------------------------------+ | Inspecting the data set before estimation. | | These errors mark observations which will be skipped. | | Row Individual = 1st row then group number of data block | +----------------------------------------------------------+ No bad observations were found in the sample Sample proportions are marginal, not conditional. Choices marked with * are excluded for the IIA test. +----------------+------+ |Choice (prop.)| Count| +----------------+------+ |H .18447| 449| |C .17707| 431| |P .19721| 480| |I .17502| 426| |G .14749| 359| |N .11873| 289| +----------------+------+ +---------------------------------------------+ | Discrete Choice (One Level) Model | | Model Simulation Using Previous Estimates | | Number of observations 2434 | +---------------------------------------------+ +------------------------------------------------------+ |Simulations of Probability Model | |Model: Discrete Choice (One Level) Model | |Simulated choice set may be a subset of the choices. | |Number of individuals is the probability times the | |number of observations in the simulated sample. | |Column totals may be affected by rounding error. | |The model used was simulated with 2434 observations.| +------------------------------------------------------+ ------------------------------------------------------------------------- Specification of scenario 1 is: Attribute Alternatives affected Change type Value --------- ------------------------------- ------------------- --------- RL_C1 C Fix base at new vlu 1.000 RL_C2 C Fix base at new vlu .000 ------------------------------------------------------------------------- The simulator located 2434 observations for this scenario. Simulated Probabilities (shares) for this scenario: +----------+--------------+--------------+------------------+ |Choice | Base | Scenario | Scenario - Base | | |%Share Number |%Share Number |ChgShare ChgNumber| +----------+--------------+--------------+------------------+ |H | 18.447 449 | 17.950 437 | -.497% -12 | |C | 17.707 431 | 19.889 484 | 2.182% 53 | |P | 19.721 480 | 19.142 466 | -.579% -14 | |I | 17.502 426 | 17.095 416 | -.407% -10 | |G | 14.749 359 | 14.335 349 | -.415% -10 | |N | 11.873 289 | 11.589 282 | -.284% -7 | |Total |100.000 2434 |100.000 2434 | .000% 0 | +----------+--------------+--------------+------------------+ From wgreene at stern.nyu.edu Mon Jul 6 05:22:58 2020 From: wgreene at stern.nyu.edu (William Greene) Date: Sun, 5 Jul 2020 15:22:58 -0400 Subject: [Limdep Nlogit List] Simulation using estimates from SP data and ASCs from RP data In-Reply-To: References: Message-ID: The same subsample, |-> sample;all$ |-> reject;sprp=1$ is being used for both estimation and simulation. It sounds like you want to fit the model with one subset of the data and do the simulation with a different one. That is OK, just set the samples accordingly for the two cases. /B. Greene On Fri, Jul 3, 2020 at 10:31 PM Thao Thai via Limdep < limdep at mailman.sydney.edu.au> wrote: > Hi Nlogit users, > > I am simulating some scenarios using the coefficient estimates from a CL > model on SP data and alternative specific constants (ASCs) calibrated to > reflect the RP data. > > However, the reported market shares in the base case shows the market > shares observed in SP data. The reported market shares of the simulated > scenarios are also compared to these SP data market shares, which I think > is not correct (It should be compared to the market shares from RP data)? > > Could you please kindly let me know if my syntax below is correct? > > Thank you so much for your help! > Thao > > |-> sample;all$ > |-> reject;sprp=1$ > |-> Nlogit > ;lhs = cho, cset, alti > ;choices = H, C, P, I, G, N > ;crosstabs > ;checkdata > ;model: > U(H) = rl_h1 * RL_H1 + rl_h2 * RL_H2 > + fl_ * FL_H > + cr_h1 * CR_H1 + cr_2 * CR_H2 > + lo_h1 * LO_H1 > + sa_ * SA_H > / > U(C) = com > + rl_c1 * RL_C1 + rl_c2 * RL_C2 > + fl_ * FL_C > + cr_c1 * CR_C1 + cr_2 * CR_C2 > + lo_c1 * LO_C1 + lo_c2 * LO_C2 > + sa_ * SA_C/ > U(P) = pri > + rl_p1 * RL_P1 > + fl_ * FL_P > + cr_p1 * CR_P1 + cr_2 * CR_P2 > + lo_p1 * LO_P1 + lo_p2 * LO_P2 > + sa_ * SA_P/ > U(I) = ind > + rl_i1 * RL_I1 + rl_i2 * RL_I2 > + fl_ * FL_I > + cr_i1 * CR_I1 > + lo_i1 * LO_I1 > + sa_ * SA_I/ > U(G) = gov > + rl_g1 * RL_G1 > + fl_ * FL_G > + cr_g1 * CR_G1 > + lo_g1 * LO_G1 > + sa_ * SA_G/ > U(N) = non > + rl_n1 * RL_N1 > + fl_ * FL_N > + cr_n1 * CR_N1 > + lo_n1 * LO_N1 + lo_n2 * LO_N2 > + sa_ * SA_N > $ > +----------------------------------------------------------+ > | Inspecting the data set before estimation. | > | These errors mark observations which will be skipped. | > | Row Individual = 1st row then group number of data block | > +----------------------------------------------------------+ > No bad observations were found in the sample > > Iterative procedure has converged > Normal exit: 5 iterations. Status=0, F= .4001834D+04 > > > ----------------------------------------------------------------------------- > Discrete choice (multinomial logit) model > Dependent variable Choice > Log likelihood function -4001.83388 > Estimation based on N = 2434, K = 32 > Inf.Cr.AIC = 8067.7 AIC/N = 3.315 > --------------------------------------- > Log likelihood R-sqrd R2Adj > ASCs only model must be fit separately > Use NLOGIT ;...;RHS=ONE$ > Note: R-sqrd = 1 - logL/Logl(constants) > --------------------------------------- > Chi-squared[27] = 655.81735 > Prob [ chi squared > value ] = .00000 > Response data are given as ind. choices > Number of obs.= 2434, skipped 0 obs > > --------+-------------------------------------------------------------------- > | Standard Prob. 95% Confidence > CHO| Coefficient Error z |z|>Z* Interval > > --------+-------------------------------------------------------------------- > RL_H1| .07005 .16175 .43 .6650 -.24697 .38707 > RL_H2| .23104* .13433 1.72 .0855 -.03225 .49433 > FL_| .17867*** .05585 3.20 .0014 .06920 .28813 > CR_H1| .27104** .13131 2.06 .0390 .01368 .52840 > CR_2| .05296 .09477 .56 .5763 -.13279 .23871 > LO_H1| -.41466*** .12119 -3.42 .0006 -.65219 -.17713 > SA_| .01231*** .00086 14.30 .0000 .01062 .01399 > COM| -.10806 .15871 -.68 .4959 -.41913 .20300 > RL_C1| .38787** .16751 2.32 .0206 .05956 .71618 > RL_C2| .26364* .15216 1.73 .0832 -.03459 .56187 > CR_C1| .19661 .13691 1.44 .1510 -.07174 .46496 > LO_C1| -.22246* .12854 -1.73 .0835 -.47440 .02949 > LO_C2| -.82061*** .15800 -5.19 .0000 -1.13027 -.51094 > PRI| .40590*** .14002 2.90 .0037 .13146 .68034 > RL_P1| .01672 .12447 .13 .8932 -.22725 .26068 > CR_P1| .33357** .13132 2.54 .0111 .07619 .59095 > LO_P1| -.95156*** .14221 -6.69 .0000 -1.23029 -.67282 > LO_P2| -1.03276*** .14087 -7.33 .0000 -1.30885 -.75667 > IND| -1.11685*** .15286 -7.31 .0000 -1.41645 -.81725 > RL_I1| .64312*** .14665 4.39 .0000 .35570 .93054 > RL_I2| .79032*** .15550 5.08 .0000 .48555 1.09508 > CR_I1| .61024*** .12199 5.00 .0000 .37115 .84933 > LO_I1| -.64615*** .12354 -5.23 .0000 -.88828 -.40402 > GOV| -.07470 .14043 -.53 .5948 -.34993 .20053 > RL_G1| -.33174** .12927 -2.57 .0103 -.58510 -.07838 > CR_G1| .51684*** .12294 4.20 .0000 .27588 .75781 > LO_G1| -.57783*** .12464 -4.64 .0000 -.82212 -.33354 > NON| -.26651* .14521 -1.84 .0665 -.55112 .01810 > RL_N1| -.08143 .13884 -.59 .5576 -.35355 .19070 > CR_N1| .36292*** .13942 2.60 .0092 .08965 .63618 > LO_N1| -.58506*** .15904 -3.68 .0002 -.89677 -.27336 > LO_N2| -.46639*** .16171 -2.88 .0039 -.78333 -.14945 > > --------+-------------------------------------------------------------------- > ***, **, * ==> Significance at 1%, 5%, 10% level. > Model was estimated on Jul 04, 2020 at 00:17:29 PM > > ----------------------------------------------------------------------------- > > |-> sample;all$ > |-> reject;sprp=1$ > |-> Nlogit > ;lhs = cho, cset, alti > ;choices = H, C, P, I, G, N/0.23,0.53,0.04,0.04,0.09,0.07 > ;checkdata > ;show > 'Alg=BFGS > ;calibrate > ; simulation > ; scenario: RL_C1(c)=1/RL_C2(c)=0 > ;model: > U(H) = rl_h1[0.0700481] * RL_H1 + rl_h2[0.231038] * RL_H2 > + fl_[0.178665] * FL_H > + cr_h1[0.271042] * CR_H1 + cr_2[0.0529584] * CR_H2 > + lo_h1[-0.414659] * LO_H1 > + sa_[0.0123069] * SA_H > / > U(C) = com > + rl_c1[0.387869] * RL_C1 + rl_c2[0.263637] * RL_C2 > + fl_[0.178665] * FL_C > + cr_c1[0.19661] * CR_C1 + cr_2[0.0529584] * CR_C2 > + lo_c1[-0.222456] * LO_C1 + lo_c2[-0.820609] * LO_C2 > + sa_[0.0123069] * SA_C > / > U(P) = pri > + rl_p1[0.0167174] * RL_P1 > + fl_[0.178665] * FL_P > + cr_p1[0.333571] * CR_P1 + cr_2[0.0529584] * CR_P2 > + lo_p1[-0.951557] * LO_P1 + lo_p2[-1.03276] * LO_P2 > + sa_[0.0123069] * SA_P > / > U(I) = ind > + rl_i1[0.643119] * RL_I1 + rl_i2[0.790318] * RL_I2 > + fl_[0.178665] * FL_I > + cr_i1[0.610239] * CR_I1 > + lo_i1[-0.64615] * LO_I1 > + sa_[0.0123069] * SA_I > / > U(G) = gov > + rl_g1[-0.331743] * RL_G1 > + fl_[0.178665] * FL_G > + cr_g1[0.516844] * CR_G1 > + lo_g1[-0.57783] * LO_G1 > + sa_[0.0123069] * SA_G > / > U(N) = non > + rl_n1[-0.0814267] * RL_N1 > + fl_[0.178665] * FL_N > + cr_n1[0.36292] * CR_N1 > + lo_n1[-0.585064] * LO_N1 + lo_n2[-0.466387] * LO_N2 > + sa_[0.0123069] * SA_N$ > +----------------------------------------------------------+ > | Inspecting the data set before estimation. | > | These errors mark observations which will be skipped. | > | Row Individual = 1st row then group number of data block | > +----------------------------------------------------------+ > No bad observations were found in the sample > > > Sample proportions are marginal, not conditional. > Choices marked with * are excluded for the IIA test. > +----------------+------+ > |Choice (prop.)| Count| > +----------------+------+ > |H .18447| 449| > |C .17707| 431| > |P .19721| 480| > |I .17502| 426| > |G .14749| 359| > |N .11873| 289| > +----------------+------+ > > > +---------------------------------------------+ > | Discrete Choice (One Level) Model | > | Model Simulation Using Previous Estimates | > | Number of observations 2434 | > +---------------------------------------------+ > > +------------------------------------------------------+ > |Simulations of Probability Model | > |Model: Discrete Choice (One Level) Model | > |Simulated choice set may be a subset of the choices. | > |Number of individuals is the probability times the | > |number of observations in the simulated sample. | > |Column totals may be affected by rounding error. | > |The model used was simulated with 2434 observations.| > +------------------------------------------------------+ > ------------------------------------------------------------------------- > Specification of scenario 1 is: > Attribute Alternatives affected Change type Value > --------- ------------------------------- ------------------- --------- > RL_C1 C Fix base at new vlu 1.000 > RL_C2 C Fix base at new vlu .000 > ------------------------------------------------------------------------- > The simulator located 2434 observations for this scenario. > Simulated Probabilities (shares) for this scenario: > +----------+--------------+--------------+------------------+ > |Choice | Base | Scenario | Scenario - Base | > | |%Share Number |%Share Number |ChgShare ChgNumber| > +----------+--------------+--------------+------------------+ > |H | 18.447 449 | 17.950 437 | -.497% -12 | > |C | 17.707 431 | 19.889 484 | 2.182% 53 | > |P | 19.721 480 | 19.142 466 | -.579% -14 | > |I | 17.502 426 | 17.095 416 | -.407% -10 | > |G | 14.749 359 | 14.335 349 | -.415% -10 | > |N | 11.873 289 | 11.589 282 | -.284% -7 | > |Total |100.000 2434 |100.000 2434 | .000% 0 | > +----------+--------------+--------------+------------------+ > _______________________________________________ > Limdep site list > Limdep at mailman.sydney.edu.au > http://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/fnofCWLVXkU9jgOjF62sjW?domain=people.stern.nyu.edu Email: wgreene at stern.nyu.edu Ph. +1.646.596.3296 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 From john.c.whitehead at gmail.com Tue Jul 7 00:58:35 2020 From: john.c.whitehead at gmail.com (John C. Whitehead) Date: Mon, 6 Jul 2020 10:58:35 -0400 Subject: [Limdep Nlogit List] restrictions on coefficients in mixed logit, etc Message-ID: Hi all, I'm having trouble with restrictions on coefficients in all discrete choice models except the latent class logit. For example, if I estimate the following model: lclogit;Lhs=Y;Choices=yes,no;Rhs=x,y,z;pds=3;lcm;pts=2; rst=b1,b2,b3,0,0,0$ NLogit will constrain the coefficients in the second class to zero. But, if I estimate the scaled model: smnlogit;Lhs=Y;Choices=yes,no;Rhs=x,y,z;pds=3;halton;pts=25; rst=0,b2,b3$ It produces output as if the restriction was not given (i.e., it estimates a number for b1). I've searched the manual and it says restrictions should work in these models (unless I've missed something, I haven't read every word). Any help would be appreciated! Thanks, John Whitehead From avassilopoulos.aua at gmail.com Tue Jul 7 20:15:16 2020 From: avassilopoulos.aua at gmail.com (Achilleas' Gmail) Date: Tue, 7 Jul 2020 13:15:16 +0300 Subject: [Limdep Nlogit List] restrictions on coefficients in mixed logit, etc In-Reply-To: References: Message-ID: <000001d65447$844f77a0$8cee66e0$@gmail.com> Hi, I've noticed the same. Neither "; CML:" seems to work with smnlogit. Best, Achilleas Vassilopoulos -----Original Message----- From: Limdep On Behalf Of John C. Whitehead via Limdep Sent: Monday, July 6, 2020 17:59 To: Limdep and Nlogit Mailing List Cc: John C. Whitehead Subject: [Limdep Nlogit List] restrictions on coefficients in mixed logit, etc Hi all, I'm having trouble with restrictions on coefficients in all discrete choice models except the latent class logit. For example, if I estimate the following model: lclogit;Lhs=Y;Choices=yes,no;Rhs=x,y,z;pds=3;lcm;pts=2; rst=b1,b2,b3,0,0,0$ NLogit will constrain the coefficients in the second class to zero. But, if I estimate the scaled model: smnlogit;Lhs=Y;Choices=yes,no;Rhs=x,y,z;pds=3;halton;pts=25; rst=0,b2,b3$ It produces output as if the restriction was not given (i.e., it estimates a number for b1). I've searched the manual and it says restrictions should work in these models (unless I've missed something, I haven't read every word). Any help would be appreciated! Thanks, John Whitehead _______________________________________________ Limdep site list Limdep at mailman.sydney.edu.au http://limdep.itls.usyd.edu.au From avassilopoulos.aua at gmail.com Tue Jul 7 22:30:20 2020 From: avassilopoulos.aua at gmail.com (Achilleas' Gmail) Date: Tue, 7 Jul 2020 15:30:20 +0300 Subject: [Limdep Nlogit List] restrictions on coefficients in mixed logit, etc References: Message-ID: <000801d6545a$61d754a0$2585fde0$@gmail.com> Hello again, Coming back to this issue, "; Fix = name[0] " seems to be working well with smnlogit. namelist ; x = gc,ttme,invc,invt,one $ smnlogit ; Lhs = Mode ; Choices = air,train,bus,car ; Rhs = x ; Fix = ttme[0] $ Best, Achilleas Vassilopoulos -----Original Message----- From: Achilleas' Gmail Sent: Tuesday, July 7, 2020 13:15 To: 'Limdep and Nlogit Mailing List' Subject: RE: [Limdep Nlogit List] restrictions on coefficients in mixed logit, etc Hi, I've noticed the same. Neither "; CML:" seems to work with smnlogit. Best, Achilleas Vassilopoulos -----Original Message----- From: Limdep On Behalf Of John C. Whitehead via Limdep Sent: Monday, July 6, 2020 17:59 To: Limdep and Nlogit Mailing List Cc: John C. Whitehead Subject: [Limdep Nlogit List] restrictions on coefficients in mixed logit, etc Hi all, I'm having trouble with restrictions on coefficients in all discrete choice models except the latent class logit. For example, if I estimate the following model: lclogit;Lhs=Y;Choices=yes,no;Rhs=x,y,z;pds=3;lcm;pts=2; rst=b1,b2,b3,0,0,0$ NLogit will constrain the coefficients in the second class to zero. But, if I estimate the scaled model: smnlogit;Lhs=Y;Choices=yes,no;Rhs=x,y,z;pds=3;halton;pts=25; rst=0,b2,b3$ It produces output as if the restriction was not given (i.e., it estimates a number for b1). I've searched the manual and it says restrictions should work in these models (unless I've missed something, I haven't read every word). Any help would be appreciated! Thanks, John Whitehead _______________________________________________ Limdep site list Limdep at mailman.sydney.edu.au http://limdep.itls.usyd.edu.au From john.c.whitehead at gmail.com Wed Jul 8 10:34:42 2020 From: john.c.whitehead at gmail.com (John C. Whitehead) Date: Tue, 7 Jul 2020 20:34:42 -0400 Subject: [Limdep Nlogit List] restrictions on coefficients in mixed logit, etc In-Reply-To: <000801d6545a$61d754a0$2585fde0$@gmail.com> References: <000801d6545a$61d754a0$2585fde0$@gmail.com> Message-ID: ;Fix = x[0]$ is working for me with all models! But, I'd still like to constrain some parameters to be equal ... On Tue, Jul 7, 2020 at 8:30 AM Achilleas' Gmail via Limdep < limdep at mailman.sydney.edu.au> wrote: > Hello again, > > Coming back to this issue, "; Fix = name[0] " seems to be working well > with > smnlogit. > > namelist ; x = gc,ttme,invc,invt,one $ > smnlogit ; Lhs = Mode ; Choices = air,train,bus,car > ; Rhs = x > ; Fix = ttme[0] $ > > Best, > Achilleas Vassilopoulos > > -----Original Message----- > From: Achilleas' Gmail > Sent: Tuesday, July 7, 2020 13:15 > To: 'Limdep and Nlogit Mailing List' > Subject: RE: [Limdep Nlogit List] restrictions on coefficients in mixed > logit, etc > > Hi, I've noticed the same. > > Neither "; CML:" seems to work with smnlogit. > > Best, > Achilleas Vassilopoulos > > -----Original Message----- > From: Limdep On Behalf Of John C. > Whitehead via Limdep > Sent: Monday, July 6, 2020 17:59 > To: Limdep and Nlogit Mailing List > Cc: John C. Whitehead > Subject: [Limdep Nlogit List] restrictions on coefficients in mixed logit, > etc > > Hi all, > > I'm having trouble with restrictions on coefficients in all discrete choice > models except the latent class logit. > > For example, if I estimate the following model: > > lclogit;Lhs=Y;Choices=yes,no;Rhs=x,y,z;pds=3;lcm;pts=2; > rst=b1,b2,b3,0,0,0$ > > NLogit will constrain the coefficients in the second class to zero. > > But, if I estimate the scaled model: > > smnlogit;Lhs=Y;Choices=yes,no;Rhs=x,y,z;pds=3;halton;pts=25; > rst=0,b2,b3$ > > It produces output as if the restriction was not given (i.e., it estimates > a > number for b1). > > I've searched the manual and it says restrictions should work in these > models (unless I've missed something, I haven't read every word). > > Any help would be appreciated! > > Thanks, > > John Whitehead > _______________________________________________ > Limdep site list > Limdep at mailman.sydney.edu.au > http://limdep.itls.usyd.edu.au > > > _______________________________________________ > Limdep site list > Limdep at mailman.sydney.edu.au > http://limdep.itls.usyd.edu.au > > From david.hensher at sydney.edu.au Wed Jul 8 10:36:58 2020 From: david.hensher at sydney.edu.au (David Hensher) Date: Wed, 08 Jul 2020 10:36:58 +1000 Subject: [Limdep Nlogit List] restrictions on coefficients in mixed logit, etc In-Reply-To: References: <000801d6545a$61d754a0$2585fde0$@gmail.com> Message-ID: <5F05152A.3000306@sydney.edu.au> To make betas equal give them the same names David On 8/07/2020 10:34 AM, John C. Whitehead via Limdep wrote: > ;Fix = x[0]$ is working for me with all models! > > But, I'd still like to constrain some parameters to be equal ... > > On Tue, Jul 7, 2020 at 8:30 AM Achilleas' Gmail via Limdep< > limdep at mailman.sydney.edu.au> wrote: > > >> Hello again, >> >> Coming back to this issue, "; Fix = name[0] " seems to be working well >> with >> smnlogit. >> >> namelist ; x = gc,ttme,invc,invt,one $ >> smnlogit ; Lhs = Mode ; Choices = air,train,bus,car >> ; Rhs = x >> ; Fix = ttme[0] $ >> >> Best, >> Achilleas Vassilopoulos >> >> -----Original Message----- >> From: Achilleas' Gmail >> Sent: Tuesday, July 7, 2020 13:15 >> To: 'Limdep and Nlogit Mailing List' >> Subject: RE: [Limdep Nlogit List] restrictions on coefficients in mixed >> logit, etc >> >> Hi, I've noticed the same. >> >> Neither "; CML:" seems to work with smnlogit. >> >> Best, >> Achilleas Vassilopoulos >> >> -----Original Message----- >> From: Limdep On Behalf Of John C. >> Whitehead via Limdep >> Sent: Monday, July 6, 2020 17:59 >> To: Limdep and Nlogit Mailing List >> Cc: John C. Whitehead >> Subject: [Limdep Nlogit List] restrictions on coefficients in mixed logit, >> etc >> >> Hi all, >> >> I'm having trouble with restrictions on coefficients in all discrete choice >> models except the latent class logit. >> >> For example, if I estimate the following model: >> >> lclogit;Lhs=Y;Choices=yes,no;Rhs=x,y,z;pds=3;lcm;pts=2; >> rst=b1,b2,b3,0,0,0$ >> >> NLogit will constrain the coefficients in the second class to zero. >> >> But, if I estimate the scaled model: >> >> smnlogit;Lhs=Y;Choices=yes,no;Rhs=x,y,z;pds=3;halton;pts=25; >> rst=0,b2,b3$ >> >> It produces output as if the restriction was not given (i.e., it estimates >> a >> number for b1). >> >> I've searched the manual and it says restrictions should work in these >> models (unless I've missed something, I haven't read every word). >> >> Any help would be appreciated! >> >> Thanks, >> >> John Whitehead >> _______________________________________________ >> Limdep site list >> Limdep at mailman.sydney.edu.au >> http://limdep.itls.usyd.edu.au >> >> >> _______________________________________________ >> Limdep site list >> Limdep at mailman.sydney.edu.au >> http://limdep.itls.usyd.edu.au >> >> >> > _______________________________________________ > Limdep site list > Limdep at mailman.sydney.edu.au > http://limdep.itls.usyd.edu.au > > -- DAVID HENSHER FASSA, PhD| Professor and Founding Director Institute of Transport and Logistics Studies | The University of Sydney Business School THE UNIVERSITY OF SYDNEY Rm 201, Building H73| The University of Sydney | NSW | 2006 Street Address: 378 Abercrombie St, Darlington NSW 2008 T +61 2 9114 1871 | F +61 2 9114 1863 | M +61 418 433 057 E David.Hensher at sydney.edu.au | W sydney.edu.au/business/itls |W http://sydney.edu.au/business/itls/staff/davidh Celebrating 30 years of ITLS: 1991-2020 https://protect-au.mimecast.com/s/agrmCNLJyQUloMzJIm66Ac?domain=youtu.be ERA Rank 5 (Transportation and Freight Services) Co-Founder of the International Conference Series on Competition and Ownership of Land Passenger Transport (The 'Thredbo' Series) https://protect-au.mimecast.com/s/b4kNCOMKzVTR32MlHvBdnQ?domain=thredbo-conference-series.org https://www.linkedin.com/company/28450714 Second edition of Applied Choice Analysis now available at https://protect-au.mimecast.com/s/ebYYCP7LAXfGQ5P9U1C9AY?domain=cambridge.org Nlogit is the most popular software for choice modellers. See https://protect-au.mimecast.com/s/hEYDCQnMBZfA7NYgtkMmi2?domain=limdep.com CRICOS 00026A This email plus any attachments to it are confidential. Any unauthorised use is strictly prohibited. If you receive this email in error, please delete it and any attachments. Please think of our environment and only print this e-mail if necessary. From sdsuh at hotmail.com Sat Jul 18 13:41:07 2020 From: sdsuh at hotmail.com (Daniel Suh) Date: Sat, 18 Jul 2020 03:41:07 +0000 Subject: [Limdep Nlogit List] NLOGIT 4, dialog box language issue Message-ID: Hello, I am having dialog boxes in Japanese language whenever I run NLOGIT (version 4). It is very peculiar since all NLOGIT program menus are in English. I do not have Japanese in window(windows 10, 64 bit) language settings. I remember I did have the same issue some year back, and somehow I did resolve the issue. Problem is I do not remember what I did that time, and all the google searches now do not give me any clue. Could you somebody help me with this?? Thanks. From florian.neubauer at uconn.edu Wed Jul 29 01:15:14 2020 From: florian.neubauer at uconn.edu (Florian Neubauer) Date: Tue, 28 Jul 2020 11:15:14 -0400 Subject: [Limdep Nlogit List] Export to Excel Message-ID: Hi all, I am completely new to Limdep/Nlogit. I?m estimating a stochastic frontier model and want to export the results to an Excel file instead of doing everything manually. Is that possible and if so, could you send me an example of the code? Thanks, Florian From avassilopoulos.aua at gmail.com Wed Jul 29 20:01:48 2020 From: avassilopoulos.aua at gmail.com (Achilleas' Gmail) Date: Wed, 29 Jul 2020 13:01:48 +0300 Subject: [Limdep Nlogit List] Export to Excel In-Reply-To: References: Message-ID: <003301d6658f$46f88870$d4e99950$@gmail.com> Hi Florian, Try the following: OPEN ; Export = "YOUR_PATH\RESULTS.csv" $ FRONTIER ; Lhs = y ; Rhs = x ; Export $ CLOSE ; Export $ Best, Achilleas -----Original Message----- From: Limdep On Behalf Of Florian Neubauer Sent: Tuesday, July 28, 2020 18:15 To: limdep at mailman.sydney.edu.au Subject: [Limdep Nlogit List] Export to Excel Hi all, I am completely new to Limdep/Nlogit. I?m estimating a stochastic frontier model and want to export the results to an Excel file instead of doing everything manually. Is that possible and if so, could you send me an example of the code? Thanks, Florian _______________________________________________ Limdep site list Limdep at mailman.sydney.edu.au http://limdep.itls.usyd.edu.au