MCMC1: MCMC algorithm to sample from bivariate probit with partial...

In BiProbitPartial: Bivariate Probit with Partial Observability

Description

MCMC1() produces MCMC draws from the posterior of the bivariate probit with partial observability. It does not perform input validation. It is reccomended to use BiProbitPartial instead of this function. BiProbitPartial performs input validation and then calls this function if philosophy == "bayesian".

Usage

MCMC1(X1, X2, Z, beta1, beta2, rho, fixrho, S, beta0, B0inv, rho0, v0, nu,
  P, tauSq, seed)

Arguments

X1	a matrix of covariates for the first equation
X2	a matrix of covariates for the second equation
Z	a matrix of response values
beta1	a matrix of starting values for beta1
beta2	a matrix of starting values for beta2
rho	a numeric starting value for rho
fixrho	a logical determining if rho is fixed
S	a numeric for the number of MCMC iterations
beta0	a matrix of the beta prior mean parameter
B0inv	a matrix of the inverse of beta prior covariance parameter
rho0	a numeric for the mu prior parameter for rho
v0	a numeric for the Sigma prior parameter for rho
nu	a numeric for MCMC tuning parameter 1
P	a numeric for MCMC tuning parameter 2
tauSq	a numeric for MCMC tuning parameter 3
seed	a numeric seed for determining the random draw sequence

Value

a matrix of MCMC draws

BiProbitPartial documentation built on May 2, 2019, 1:48 p.m.