llmnp {bayesm}

Evaluate Log Likelihood for Multinomial Probit Model

Description

llmnp evaluates the log-likelihood for the multinomial probit model.

Usage

llmnp(beta, Sigma, X, y, r)

Arguments

beta

k x 1 vector of coefficients

Sigma

(p-1) x (p-1) covariance matrix of errors

X

n*(p-1) x k array where X is from differenced system

y

vector of n indicators of multinomial response (1, ..., p)

r

number of draws used in GHK

Details

X is (p-1)*n x k matrix. Use createX with DIFF=TRUE to create X.

Model for each obs: w = Xbeta + e with e \sim N(0,Sigma).

Censoring mechanism:
if y=j (j<p), w_j > max(w_{-j}) and w_j >0
if y=p, w < 0

To use GHK, we must transform so that these are rectangular regions e.g. if y=1, w_1 > 0 and w_1 - w_{-1} > 0.

Define A_j such that if j=1,\ldots,p-1 then A_jw = A_jmu + A_je > 0 is equivalent to y=j. Thus, if y=j, we have A_je > -A_jmu. Lower truncation is -A_jmu and cov = A_jSigmat(A_j). For j=p, e < - mu.

Value

Value of log-likelihood (sum of log prob of observed multinomial outcomes)

Warning

This routine is a utility routine that does not check the input arguments for proper dimensions and type.

Author(s)

Peter Rossi, Anderson School, UCLA, [e-mail].

References

For further discussion, see Chapters 2 and 4, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.

See Also

createX, rmnpGibbs

Examples

## Not run: ll=llmnp(beta,Sigma,X,y,r)