From djourdain at ait.asia Wed Apr 4 00:56:50 2018
From: djourdain at ait.asia (Damien Jourdain)
Date: Tue, 3 Apr 2018 16:56:50 +0200
Subject: [Limdep Nlogit List] Simulating unconditional WTP
Message-ID: <005401d3cb5c$00df99c0$029ecd40$@cirad.fr>
Dear All,
I ran the following RPL-EC model. It gives me reasonable results.
? ML-EC Model
Sample; ALL$
Calc; Ran(12345)$
RPLOGIT
; Choices = 1,2,3
; Lhs = CHOICE, CSET, ALT
; Model:
U(1) = A1 + BE*BENEF + LA*LAB + CA*CASH +RI*RISK + FEL*FERTLO +FEH*
FERTHI /
U(2,3) = BE*BENEF + LA*LAB + CA*CASH +RI*RISK + FEL*FERTLO +FEH*
FERTHI
; Fcn = BE(l), LA(n), CA(n), RI(n), FEH(n)
; Pds = 6
; Pts = 20
; Shuffled
; Correlation
; WTP = LA/BE, CA/BE, RI/BE, FEH/BE
; ECM = (2,3)
; Parameters
; Maxit = 300$
I have obtained the conditional WTP (using the WTP command) but in fact I
would like to manipulate simulated un-conditional WTPs. As the coefficients
are correlated, I understand that I have to use the Cholesky decomposition
(provided in the model output) to be able to draw correlated random
coefficients. I have two set of questions:
First, can I use the WALD command to develop these simulations (I know this
can be done with an MNL model, but I do not see any simple manner to use the
B and VARB matrices obtained from the model for a RPL model.
Second, as I assumed that I could not use WALD, I developed the following
commands for simulating the first three random coefficients:
SAMPLE; 1-1000$
create
; rna1 = rnn(0,1)
; rna2 = rnn(0,1)
; rna3 = rnn(0,1)
; rna4 = rnn(0,1)
; rna5 = rnn(0,1)
; rna6 = rnn(0,1)
; MUBEN = B(1) + B(8) * RNA1
; MULAB = B(2) + B(13) * RNA2 + B(9) * RNA3
; MUCAS = B(3) + B(14) * RNA4 + B(15) * RNA5 + B(10) * RNA6
$
(the coefficients are correct and correspond to the Cholesky matrix
coefficients )
When checking the simulated coefficients, I can reproduce the coefficients
mean and standard deviation, but they are not correlated as I had expected
given that I used the Cholesky coefficients to reproduce these correlations
I do not understand what I have done wrong?
Any help is welcomed.
Damien
DSTAT; RHS = MUBEN, MULAB, MUCAS; out=2$
--------+-------------------------------------------------------------------
--
| Standard
Missing
Variable| Mean Deviation Minimum Maximum Cases
Values
--------+-------------------------------------------------------------------
--
MUBEN| -3.7348 .408152 -5.023775 -2.357132 1000
0
MULAB| -.02753 .028474 -.117069 .048933 1000
0
MUCAS| -.015962 .015583 -.064371 .035926 1000
0
--------+-------------------------------------------------------------------
--
Descriptive Statistics for 3 variables
DSTAT results are matrix LASTDSTA in current project.
Correlations computed for 3 variables.
--------+--------------------------
Cor.Mat.| MUBEN MULAB MUCAS
--------+--------------------------
MUBEN| 1.00000 .04327 -.04466
MULAB| .04327 1.00000 -.01857
MUCAS| -.04466 -.01857 1.00000
I am really far off the model outputs
--------+--------------------------------------------
Cor.Mat.| ONE LA CA RI FEH
--------+--------------------------------------------
ONE| 1.00000 -.61706 -.83717 .07331 -.06425
LA| -.61706 1.00000 .23113 -.52217 -.23501
CA| -.83717 .23113 1.00000 .09078 -.16867
RI| .07331 -.52217 .09078 1.00000 .63762
FEH| -.06425 -.23501 -.16867 .63762 1.00000
Dr. Damien Jourdain
Agricultural and Natural Resources Economist
Visiting Assistant Professor
Asian Institute of Technology / CIRAD G-EAU
Natural Resource Management / Water Engineering and Management
From wgreene at stern.nyu.edu Wed Apr 4 01:13:10 2018
From: wgreene at stern.nyu.edu (William Greene)
Date: Tue, 3 Apr 2018 11:13:10 -0400
Subject: [Limdep Nlogit List] Simulating unconditional WTP
In-Reply-To: <005401d3cb5c$00df99c0$029ecd40$@cirad.fr>
References: <005401d3cb5c$00df99c0$029ecd40$@cirad.fr>
Message-ID:
Damien. The simulated coefficients are not correlated because you used
independent random draws to create them. You should be using rna1 in the
first one, rna1,rna2 in the second one and rna1,rna2,rna3 in the third one.
/B, Greene
On Tue, Apr 3, 2018 at 10:56 AM, Damien Jourdain wrote:
> Dear All,
>
>
>
> I ran the following RPL-EC model. It gives me reasonable results.
>
> ? ML-EC Model
>
> Sample; ALL$
>
> Calc; Ran(12345)$
>
> RPLOGIT
>
> ; Choices = 1,2,3
>
> ; Lhs = CHOICE, CSET, ALT
>
> ; Model:
>
> U(1) = A1 + BE*BENEF + LA*LAB + CA*CASH +RI*RISK + FEL*FERTLO +FEH*
> FERTHI /
>
> U(2,3) = BE*BENEF + LA*LAB + CA*CASH +RI*RISK + FEL*FERTLO +FEH*
> FERTHI
>
> ; Fcn = BE(l), LA(n), CA(n), RI(n), FEH(n)
>
> ; Pds = 6
>
> ; Pts = 20
>
> ; Shuffled
>
> ; Correlation
>
> ; WTP = LA/BE, CA/BE, RI/BE, FEH/BE
>
> ; ECM = (2,3)
>
> ; Parameters
>
> ; Maxit = 300$
>
>
>
>
>
> I have obtained the conditional WTP (using the WTP command) but in fact I
> would like to manipulate simulated un-conditional WTPs. As the coefficients
> are correlated, I understand that I have to use the Cholesky decomposition
> (provided in the model output) to be able to draw correlated random
> coefficients. I have two set of questions:
>
>
>
> First, can I use the WALD command to develop these simulations (I know this
> can be done with an MNL model, but I do not see any simple manner to use
> the
> B and VARB matrices obtained from the model for a RPL model.
>
>
>
> Second, as I assumed that I could not use WALD, I developed the following
> commands for simulating the first three random coefficients:
>
>
>
> SAMPLE; 1-1000$
>
> create
>
> ; rna1 = rnn(0,1)
>
> ; rna2 = rnn(0,1)
>
> ; rna3 = rnn(0,1)
>
> ; rna4 = rnn(0,1)
>
> ; rna5 = rnn(0,1)
>
> ; rna6 = rnn(0,1)
>
> ; MUBEN = B(1) + B(8) * RNA1
>
> ; MULAB = B(2) + B(13) * RNA2 + B(9) * RNA3
>
> ; MUCAS = B(3) + B(14) * RNA4 + B(15) * RNA5 + B(10) * RNA6
>
> $
>
>
>
> (the coefficients are correct and correspond to the Cholesky matrix
> coefficients )
>
>
>
> When checking the simulated coefficients, I can reproduce the coefficients
> mean and standard deviation, but they are not correlated as I had expected
> given that I used the Cholesky coefficients to reproduce these correlations
>
>
>
> I do not understand what I have done wrong?
>
>
>
> Any help is welcomed.
>
>
>
>
>
>
>
> Damien
>
>
>
>
>
>
>
>
>
> DSTAT; RHS = MUBEN, MULAB, MUCAS; out=2$
>
> --------+---------------------------------------------------
> ----------------
> --
>
> | Standard
> Missing
>
> Variable| Mean Deviation Minimum Maximum Cases
> Values
>
> --------+---------------------------------------------------
> ----------------
> --
>
> MUBEN| -3.7348 .408152 -5.023775 -2.357132 1000
> 0
>
> MULAB| -.02753 .028474 -.117069 .048933 1000
> 0
>
> MUCAS| -.015962 .015583 -.064371 .035926 1000
> 0
>
> --------+---------------------------------------------------
> ----------------
> --
>
>
>
> Descriptive Statistics for 3 variables
>
> DSTAT results are matrix LASTDSTA in current project.
>
>
>
> Correlations computed for 3 variables.
>
>
>
> --------+--------------------------
>
> Cor.Mat.| MUBEN MULAB MUCAS
>
> --------+--------------------------
>
> MUBEN| 1.00000 .04327 -.04466
>
> MULAB| .04327 1.00000 -.01857
>
> MUCAS| -.04466 -.01857 1.00000
>
>
>
>
>
> I am really far off the model outputs
>
> --------+--------------------------------------------
>
> Cor.Mat.| ONE LA CA RI FEH
>
> --------+--------------------------------------------
>
> ONE| 1.00000 -.61706 -.83717 .07331 -.06425
>
> LA| -.61706 1.00000 .23113 -.52217 -.23501
>
> CA| -.83717 .23113 1.00000 .09078 -.16867
>
> RI| .07331 -.52217 .09078 1.00000 .63762
>
> FEH| -.06425 -.23501 -.16867 .63762 1.00000
>
>
>
>
>
>
>
>
>
>
>
>
>
> Dr. Damien Jourdain
>
> Agricultural and Natural Resources Economist
>
> Visiting Assistant Professor
>
> Asian Institute of Technology / CIRAD G-EAU
>
> Natural Resource Management / Water Engineering and Management
>
> _______________________________________________
> Limdep site list
> Limdep at mailman.sydney.edu.au
> http://limdep.itls.usyd.edu.au
>
>
--
William Greene
Department of Economics
Stern School of Business, New York University
44 West 4 St., 7-90
New York, NY, 10012
URL: https://protect-au.mimecast.com/s/YjmXCD1jy9twjlkLTWn5pG?domain=people.stern.nyu.edu
Email: wgreene at stern.nyu.edu
Ph. +1.212.998.0876
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
Associate Editor: Journal of Choice Modeling
From djourdain at ait.asia Wed Apr 4 01:46:09 2018
From: djourdain at ait.asia (Damien Jourdain)
Date: Tue, 3 Apr 2018 17:46:09 +0200
Subject: [Limdep Nlogit List] Simulating unconditional WTP
In-Reply-To:
References: <005401d3cb5c$00df99c0$029ecd40$@cirad.fr>
Message-ID: <006201d3cb62$e50219b0$af064d10$@cirad.fr>
Dear Prof Greene,
Thank you for your rapid spotting of my mistake!
As a follow-up question, I noticed quite similar correlations when running DSTAT for the conditional and unconditional parameters. (see below)
The simulation now works very well. However, I notice a large difference in terms of correlation for one attribute (LAB) between conditional and un-conditional parameters.
Should I draw any conclusion from these large difference in correlation for this attribute (model specification, data?)
Yours respectfully,
Damien
SAMPLE; 1-1000$
create
; rna1 = rnn(0,1)
; rna2 = rnn(0,1)
; rna3 = rnn(0,1)
; MUBEN = exp(B(1) + B(8) * RNA1) ?? This was a lognormal distribution
; MULAB = B(2) + B(13) * RNA1 + B(9) * RNA2
; MUCAS = B(3) + B(14) * RNA1 + B(15) * RNA2 + B(10) * RNA3
$
I compared the following three correlation matrices
The one obtained directly from the NLOGIT output, the one from simulated parameters,
and the one from conditional parameters
Correlation matrix (model output)
--------+--------------------------------------------
Cor.Mat.| ONE LA CA RI FEH
--------+--------------------------------------------
ONE| 1.00000 -.61706 -.83717 .07331 -.06425
LA| -.61706 1.00000 .23113 -.52217 -.23501
CA| -.83717 .23113 1.00000 .09078 -.16867
RI| .07331 -.52217 .09078 1.00000 .63762
FEH| -.06425 -.23501 -.16867 .63762 1.00000
Correlation matrix of the simulated conditional parameters
--------+--------------------------------------------
Cor.Mat.| MUBEN MULAB MUCAS MURIS MUFEH
--------+--------------------------------------------
MUBEN| 1.00000 -.59427 -.79918 .04706 -.07725
MULAB| -.59427 1.00000 .23176 -.51297 -.23958
MUCAS| -.79918 .23176 1.00000 .09940 -.18260
MURIS| .04706 -.51297 .09940 1.00000 .62600
MUFEH| -.07725 -.23958 -.18260 .62600 1.00000
Correlation matrix of the conditional parameters
--------+--------------------------------------------
Cor.Mat.| BBEN BLAB BCAS BRIS BHI
--------+--------------------------------------------
BBEN| 1.00000 -.57742 -.83278 .14806 -.07852
BLAB| -.57742 1.00000 .75064 .56024 .46689
BCAS| -.83278 .75064 1.00000 .08143 -.11306
BRIS| .14806 .56024 .08143 1.00000 .33236
BHI| -.07852 .46689 -.11306 .33236 1.00000
De : William Greene [mailto:wgreene at stern.nyu.edu]
Envoy? : Tuesday, April 03, 2018 5:13 PM
? : Limdep and Nlogit Mailing List; djourdain at ait.asia
Objet : Re: [Limdep Nlogit List] Simulating unconditional WTP
Damien. The simulated coefficients are not correlated because you used
independent random draws to create them. You should be using rna1 in the
first one, rna1,rna2 in the second one and rna1,rna2,rna3 in the third one.
/B, Greene
On Tue, Apr 3, 2018 at 10:56 AM, Damien Jourdain wrote:
Dear All,
I ran the following RPL-EC model. It gives me reasonable results.
? ML-EC Model
Sample; ALL$
Calc; Ran(12345)$
RPLOGIT
; Choices = 1,2,3
; Lhs = CHOICE, CSET, ALT
; Model:
U(1) = A1 + BE*BENEF + LA*LAB + CA*CASH +RI*RISK + FEL*FERTLO +FEH*
FERTHI /
U(2,3) = BE*BENEF + LA*LAB + CA*CASH +RI*RISK + FEL*FERTLO +FEH*
FERTHI
; Fcn = BE(l), LA(n), CA(n), RI(n), FEH(n)
; Pds = 6
; Pts = 20
; Shuffled
; Correlation
; WTP = LA/BE, CA/BE, RI/BE, FEH/BE
; ECM = (2,3)
; Parameters
; Maxit = 300$
I have obtained the conditional WTP (using the WTP command) but in fact I
would like to manipulate simulated un-conditional WTPs. As the coefficients
are correlated, I understand that I have to use the Cholesky decomposition
(provided in the model output) to be able to draw correlated random
coefficients. I have two set of questions:
First, can I use the WALD command to develop these simulations (I know this
can be done with an MNL model, but I do not see any simple manner to use the
B and VARB matrices obtained from the model for a RPL model.
Second, as I assumed that I could not use WALD, I developed the following
commands for simulating the first three random coefficients:
SAMPLE; 1-1000$
create
; rna1 = rnn(0,1)
; rna2 = rnn(0,1)
; rna3 = rnn(0,1)
; rna4 = rnn(0,1)
; rna5 = rnn(0,1)
; rna6 = rnn(0,1)
; MUBEN = B(1) + B(8) * RNA1
; MULAB = B(2) + B(13) * RNA2 + B(9) * RNA3
; MUCAS = B(3) + B(14) * RNA4 + B(15) * RNA5 + B(10) * RNA6
$
(the coefficients are correct and correspond to the Cholesky matrix
coefficients )
When checking the simulated coefficients, I can reproduce the coefficients
mean and standard deviation, but they are not correlated as I had expected
given that I used the Cholesky coefficients to reproduce these correlations
I do not understand what I have done wrong?
Any help is welcomed.
Damien
DSTAT; RHS = MUBEN, MULAB, MUCAS; out=2$
--------+-------------------------------------------------------------------
--
| Standard
Missing
Variable| Mean Deviation Minimum Maximum Cases
Values
--------+-------------------------------------------------------------------
--
MUBEN| -3.7348 .408152 -5.023775 -2.357132 1000
0
MULAB| -.02753 .028474 -.117069 .048933 1000
0
MUCAS| -.015962 .015583 -.064371 .035926 1000
0
--------+-------------------------------------------------------------------
--
Descriptive Statistics for 3 variables
DSTAT results are matrix LASTDSTA in current project.
Correlations computed for 3 variables.
--------+--------------------------
Cor.Mat.| MUBEN MULAB MUCAS
--------+--------------------------
MUBEN| 1.00000 .04327 -.04466
MULAB| .04327 1.00000 -.01857
MUCAS| -.04466 -.01857 1.00000
I am really far off the model outputs
--------+--------------------------------------------
Cor.Mat.| ONE LA CA RI FEH
--------+--------------------------------------------
ONE| 1.00000 -.61706 -.83717 .07331 -.06425
LA| -.61706 1.00000 .23113 -.52217 -.23501
CA| -.83717 .23113 1.00000 .09078 -.16867
RI| .07331 -.52217 .09078 1.00000 .63762
FEH| -.06425 -.23501 -.16867 .63762 1.00000
Dr. Damien Jourdain
Agricultural and Natural Resources Economist
Visiting Assistant Professor
Asian Institute of Technology / CIRAD G-EAU
Natural Resource Management / Water Engineering and Management
_______________________________________________
Limdep site list
Limdep at mailman.sydney.edu.au
http://limdep.itls.usyd.edu.au
--
William Greene
Department of Economics
Stern School of Business, New York University
44 West 4 St., 7-90
New York, NY, 10012
URL: https://protect-au.mimecast.com/s/2KMRCmOxDQt81Yp1tGlv5M?domain=people.stern.nyu.edu
Email: wgreene at stern.nyu.edu
Ph. +1.212.998.0876
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
Associate Editor: Journal of Choice Modeling
From richard.turner at imarketresearch.com Wed Apr 11 06:07:04 2018
From: richard.turner at imarketresearch.com (Richard Turner)
Date: Tue, 10 Apr 2018 16:07:04 -0400
Subject: [Limdep Nlogit List] Run Models by Factor or Group Level
Message-ID:
Greetings,
I have a factor/categorical variable with 52 levels. I would like to run a
model by each level. Is this possible using "PROC/ENDPROC" in NLOGIT? After
reading this documentation
,
it wasn't clear to me how to iterate over the levels of my factor
(Additionally,
my factor levels are not enumerated sequentially).
I simplified my data so that my factor variable only had two levels: 1 and
2.
I tried using the below code:
CALC ; i=1$
PROC
CALC ; List; i;i=i+1$
Sample;all$
REJECT; FACTOR_VAR#i$
DISCRETECHOICE
;LHS=CHO, CSET
;RHS=
NALvl1, NALvl2, NALvl3, NALvl4, NALvl5, NALvl6, NALvl7, NALvl8,
NALvl9, NALvl10, NALvl11, NALvl12, NALvl13, NALvl14, NALvl15$
Sample;all$
GO TO ; i <= 2 $
ENDPROC
EXECUTE
After running the code I get this output and only one model is estimated:
[CALC] I = 1.0000000
[CALC] I = 2.0000000
Calculator: Computed 2 scalar results
Error 29: REJECT:0 obs. in resulting sample. Current sample restored.
Regards,
_____________________________________________
Richard
From wgreene at stern.nyu.edu Wed Apr 11 06:58:56 2018
From: wgreene at stern.nyu.edu (William Greene)
Date: Tue, 10 Apr 2018 16:58:56 -0400
Subject: [Limdep Nlogit List] Run Models by Factor or Group Level
In-Reply-To:
References:
Message-ID:
Richard. You should be able to use the following.
Proc$
DISCRETE ; If [ Factor = i ] ; Lhs = cho,cset ; Rhs = etc... $
EndProc $
Execute ; i = 1,52 $
Nonsequential doesn't matter.
Do note that FACTOR_VAR is not a valid name - it has 10 characters.
You'll want to be sure that the factor variable has a valid name in the
proc.
Either way, your loop is only set up to run once, so it ran correctly.
Regards
Bill Greene
On Tue, Apr 10, 2018 at 4:07 PM, Richard Turner <
richard.turner at imarketresearch.com> wrote:
> Greetings,
>
> I have a factor/categorical variable with 52 levels. I would like to run a
> model by each level. Is this possible using "PROC/ENDPROC" in NLOGIT? After
> reading this documentation
> Short-Student-Manual.pdf>,
> it wasn't clear to me how to iterate over the levels of my factor
> (Additionally,
> my factor levels are not enumerated sequentially).
>
> I simplified my data so that my factor variable only had two levels: 1 and
> 2.
>
> I tried using the below code:
> CALC ; i=1$
> PROC
> CALC ; List; i;i=i+1$
> Sample;all$
> REJECT; FACTOR_VAR#i$
> DISCRETECHOICE
> ;LHS=CHO, CSET
> ;RHS=
> NALvl1, NALvl2, NALvl3, NALvl4, NALvl5, NALvl6, NALvl7, NALvl8,
> NALvl9, NALvl10, NALvl11, NALvl12, NALvl13, NALvl14, NALvl15$
> Sample;all$
> GO TO ; i <= 2 $
> ENDPROC
> EXECUTE
>
> After running the code I get this output and only one model is estimated:
> [CALC] I = 1.0000000
> [CALC] I = 2.0000000
> Calculator: Computed 2 scalar results
> Error 29: REJECT:0 obs. in resulting sample. Current sample restored.
>
> Regards,
> _____________________________________________
>
> Richard
> _______________________________________________
> Limdep site list
> Limdep at mailman.sydney.edu.au
> http://limdep.itls.usyd.edu.au
>
>
--
William Greene
Department of Economics
Stern School of Business, New York University
44 West 4 St., 7-90
New York, NY, 10012
URL: https://protect-au.mimecast.com/s/TuOBC3Q8Z2FowwR0Hqf0K2?domain=people.stern.nyu.edu
Email: wgreene at stern.nyu.edu
Ph. +1.212.998.0876
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
Associate Editor: Journal of Choice Modeling