[Limdep Nlogit List] \MNL and marginal effects with NLOGIT?
William Greene
wgreene at stern.nyu.edu
Wed Aug 13 01:11:55 EST 2008
Kandice. In earlier versions of NLOGIT, you were only given the means
of the partial effects, in the form of a table that would give them for
the four levels in a template tree for a nested logit model. I.e.,
if you have an MNL, only the lowest level would be used and the effects
at the other levels would be zero. In the version you are using, the
program notes that you are fitting an MNL which is a one level model, and
it does not use the artificial tree structure. Rather, it gives you both
means and sample standard deviations of individual specific partial effects.
When you switch to a true nested logit model, you will then get the
expected table of effects for a tree structure.
I would note, you are computing derivatives (times 100) of the probabilities,
as your partial effects. These might not be all that meaningful. You can
get elasticities, which are more naturally scaled, by changing the brackets
to parentheses in your ;EFFECTS:... specification.
/B. Greene
----- Original Message -----
From: "Kandice L. Kleiber" <kleiberk at onid.orst.edu>
To: limdep at limdep.itls.usyd.edu.au
Sent: Tuesday, August 12, 2008 11:00:41 AM GMT -05:00 US/Canada Eastern
Subject: [Limdep Nlogit List] \MNL and marginal effects with NLOGIT?
Hi ... I'm new to LimDep/NLOGIT so please bear with me! I'm trying to set
up a nested logit piece by piece (ie add in a few variables at a time).
Because I'm waiting on same data that is choice specific data, I'm basically
setting up a multinomial logit using NLOGIT. The data that I'm using is
observation specific.
I'm trying to set up the model so it's ready to use once I get the rest of
the choice specific data, but until then I would like to keep using what I
have. My problem is in calculating marginal effects. I know that if I use
the LOGIT command for my MNL I just need ;marginal effects. I think if I
had the choice specific data I could use NLOGIT and I would need ;effects
price[*] ... along those lines. But when I use ;effects in NLOGIT my output
doesn't make much sense - I'm given mean and standard deviation, which does
not look anything like the LimDep manual has. I think this is because I'm
basically taking the marginal effects of a MNL in NLOGIT. But does this
matter? My code and output is below ... price is contant across all choices
but is set up in a matrix with the value down the diagonal. Same with the
other variables.
Please help! Is there a way to continue down this path, or should I go back
to LOGIT until I get my data to create a nested logit?
Thank you!!
--> READ
; FILE = D:\LIMDEPDATA\ATTEMPT201.XLS
; NVAR = 15 ; NOBS = 1000
; NAMES = 1$
--> NLOGIT
; LHS = PROP
; CHOICES = cORNCORN, SOYCORN, CORN, SOY
; RHS = ONE, PRATIO1, PRATIO2, PRATIO3, HEL1, HEL2, HEL3, SL011, SL012,
S...
; EFFECTS: PRATIO1[*] / PRATIO2[*] / PRATIO3[*]$
+---------------------------------------------+
| Discrete choice and multinomial logit models|
+---------------------------------------------+
Normal exit from iterations. Exit status=0.
+---------------------------------------------+
| Discrete choice (multinomial logit) model |
| Maximum Likelihood Estimates |
| Model estimated: Aug 12, 2008 at 10:54:59AM.|
| Dependent variable Choice |
| Weighting variable None |
| Number of observations 106 |
| Iterations completed 5 |
| Log likelihood function -113.7578 |
| Number of parameters 12 |
| Info. Criterion: AIC = 2.37279 |
| Finite Sample: AIC = 2.40444 |
| Info. Criterion: BIC = 2.67431 |
| Info. Criterion:HQIC = 2.49500 |
| R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj |
| Constants only. Must be computed directly. |
| Use NLOGIT ;...; RHS=ONE $ |
| Chi-squared[ 9] = 1.82943 |
| Prob [ chi squared > value ] = .99389 |
| Response data are given as proportions. |
| Number of obs.= 106, skipped 0 bad obs. |
+---------------------------------------------+
+---------------------------------------------+
| Notes No coefficients=> P(i,j)=1/J(i). |
| Constants only => P(i,j) uses ASCs |
| only. N(j)/N if fixed choice set. |
| N(j) = total sample frequency for j |
| N = total sample frequency. |
| These 2 models are simple MNL models. |
| R-sqrd = 1 - LogL(model)/logL(other) |
| RsqAdj=1-[nJ/(nJ-nparm)]*(1-R-sqrd) |
| nJ = sum over i, choice set sizes |
+---------------------------------------------+
+--------+--------------+----------------+--------+--------+
|Variable| Coefficient | Standard Error |b/St.Er.|P[|Z|>z]|
+--------+--------------+----------------+--------+--------+
PRATIO1 | 65.1147789 113.523892 .574 .5663
PRATIO2 | -17.9414075 35.6312734 -.504 .6146
PRATIO3 | 11.3707783 27.9534400 .407 .6842
HEL1 | -.85499804 2.44543336 -.350 .7266
HEL2 | .03044104 .67727779 .045 .9642
HEL3 | .00948316 .53309998 .018 .9858
SL011 | -3.44864601 7.49912020 -.460 .6456
SL012 | 1.51466998 2.68596449 .564 .5728
SL013 | -.48910971 2.06371694 -.237 .8127
A_CORNCO| -64.9198907 111.509720 -.582 .5604
A_SOYCOR| 15.4826457 34.7911622 .445 .6563
A_CORN | -11.1148123 27.3848457 -.406 .6848
+---------------------------------------------------+
| Derivative (times 100) averaged over observations.|
| Attribute is PRATIO1 in choice CORNCORN |
| Effects on probabilities of all choices in model: |
| * = Direct Derivative effect of the attribute. |
| Mean St.Dev |
| * Choice=CORNCORN 84.6351 59.7107 |
| Choice=SOYCORN -12.9924 6.1500 |
| Choice=CORN -31.1550 25.5122 |
| Choice=SOY -40.4877 28.3915 |
+---------------------------------------------------+
+---------------------------------------------------+
| Derivative (times 100) averaged over observations.|
| Attribute is PRATIO1 in choice SOYCORN |
| Effects on probabilities of all choices in model: |
| * = Direct Derivative effect of the attribute. |
| Mean St.Dev |
| Choice=CORNCORN -12.9924 6.1500 |
| * Choice=SOYCORN 921.0375 157.5860 |
| Choice=CORN -376.5597 47.7207 |
| Choice=SOY -531.4850 115.8219 |
+---------------------------------------------------+
+---------------------------------------------------+
| Derivative (times 100) averaged over observations.|
| Attribute is PRATIO1 in choice CORN |
| Effects on probabilities of all choices in model: |
| * = Direct Derivative effect of the attribute. |
| Mean St.Dev |
| Choice=CORNCORN -31.1550 25.5122 |
| Choice=SOYCORN -376.5597 47.7207 |
| * Choice=CORN 1458.0870 66.6691 |
| Choice=SOY -1050.3730 91.3753 |
+---------------------------------------------------+
+---------------------------------------------------+
| Derivative (times 100) averaged over observations.|
| Attribute is PRATIO1 in choice SOY |
| Effects on probabilities of all choices in model: |
| * = Direct Derivative effect of the attribute. |
| Mean St.Dev |
| Choice=CORNCORN -40.4877 28.3915 |
| Choice=SOYCORN -531.4850 115.8219 |
| Choice=CORN -1050.3730 91.3753 |
| * Choice=SOY 1622.3460 3.2597 |
+---------------------------------------------------+
-
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