[Limdep Nlogit List] \MNL and marginal effects with NLOGIT?
Kandice L. Kleiber
kleiberk at onid.orst.edu
Wed Aug 13 01:00:41 EST 2008
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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