[R] wrong signs using MNP.

From: Kosuke Imai <kimai_at_Princeton.Edu>
Date: Sun 03 Apr 2005 - 22:36:56 EST


Dear Bill,
  You might want to check whether the parameterization of MNP differs from the book you mention, which I don't have. In particular, I would check how the choice specific covariate is calculated. More importantly, I would also make sure that your chain has converged. Based on my experiences of the multinomial probit model, 1500 draws is not sufficient. Of course, this depends on one's model, data, and starting values. On this issue, you might consult the accompanying paper as well as my Journal of Econometrics article (both available at
http://www.princeton.edu/~kimai/research/MNP.html). The former will walk you through how one might use MNP and coda packages to conduct convergence diagnostics using multiple chains via some detailed examples. Hope this helps,
Kosuke


Kosuke Imai               Office: Corwin Hall 041
Assistant Professor       Phone: 609-258-6601 
Department of Politics    eFax:  973-556-1929
Princeton University      Email: kimai@Princeton.Edu
Princeton, NJ 08544-1012 http://www.princeton.edu/~kimai

> Hi,
> I recently found the MNP package. Out of curiosity, I tried to reproduce
> results from Greene (Econometric Analysis, fourth edition) on page 874.
>
> The signs of the estimates are all opposite those of Greene's table. Might
> anyone be able to tell me what I am doing wrong?
>
> I have attached the function call, the coefficients, and a few rows of the
> data. The dataset has 210 observations.
>
> Thanks,
>
> --Bill
>
>
> res1<-mnp(mc ~ 1,
> choiceX = list(a=cbind(ttme.1,gc.1,hinc),
> b=cbind(ttme.2,gc.2,0),
> c=cbind(ttme.3,gc.3,0),
> d=cbind(ttme.4,gc.4,0)
> ),
> cXnames = c("ttme","gc","hinc"),
> data = wide,
> n.draws = 1500,
> burnin = 50,
> base = "d",
> verbose = TRUE
> )
>
> Coefficients:
> mean std.dev. 2.5% 97.5%
> (Intercept):a -1.003078 0.508752 -2.292813 -0.207
> (Intercept):b -1.511963 0.582518 -2.875069 -0.705
> (Intercept):c -1.241368 0.411084 -2.231260 -0.646
> ttme 0.022973 0.007515 0.011386 0.042
> gc 0.002175 0.001981 -0.001518 0.006
> hinc -0.030616 0.006030 -0.042062 -0.018
>
> Covariances:
> mean std.dev. 2.5% 97.5%
> a:a 1.0000 0.0000 1.0000 1.000
> a:b -0.8026 0.4655 -1.8569 -0.252
> a:c -0.7246 0.3721 -1.8966 -0.305
> b:b 1.4315 1.9552 0.1265 7.973
> b:c 1.1526 1.7011 0.1585 7.276
> c:c 1.1281 1.6327 0.2184 7.682
>
> Number of alternatives: 4
> Number of observations: 210
> Number of Gibbs draws: 1450
>
>
> The data:
>
> mc ttme.1 ttme.2 ttme.3 ttme.4 gc.1 gc.2 gc.3 gc.4 hinc
> d 69 34 35 0 70 71 70 30 35
> d 64 44 53 0 68 84 85 50 30
> d 69 34 35 0 129 195 149 101 40
> d 64 44 53 0 59 79 81 32 70
> d 64 44 53 0 82 93 94 99 45
> b 69 40 35 0 70 57 58 43 20
> a 45 34 35 0 160 213 167 125 45
> d 69 34 35 0 137 149 146 135 12
> d 69 34 35 0 70 71 70 40 40
> d 69 34 35 0 65 69 68 30 70
> d 64 44 53 0 68 70 73 36 15
> d 64 44 53 0 79 90 91 44 35
> d 64 44 53 0 63 81 83 41 50
> d 64 44 53 0 72 85 86 58 40
> d 64 44 53 0 109 214 189 199 26
> b 69 20 35 0 73 55 72 52 26
>
> .
> .
> .
>
>
>
>
>



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