bayesm’s posterior sampling function rhierMnlRwMixture permits the imposition of sign constraints on the individual-specific parameters of a hierarchical multinomial logit model. This may be desired if, for example, the researcher believes there are heterogenous effects from, say, price, but that all responses should be negative (i.e., sign-constrained). This vignette provides exposition of the model, discussion of prior specification, and an example.


The model follows the hierarchical multinomial logit specification given in Example 3 of the “bayesm Overview” Vignette, but will be repeated here succinctly. Individuals are assumed to be rational economic agents that make utility-maximizing choices. Utility is modeled as the sum of deterministic and stochastic components, where the inverse-logit of the probability of chosing an alternative is linear in the parameters and the error is assumed to follow a Type I Extreme Value distribution:

\[ U_{ij} = X_{ij}\beta_i + \varepsilon_{ij} \hspace{0.8em} \text{with} \hspace{0.8em} \varepsilon_{ij}\ \sim \text{ iid Type I EV} \]

These assumptions yield choice probabilities of:

\[ \text{Pr}(y_i=j) = \frac{\exp \{x_{ij}'\beta_i\}}{\sum_{k=1}^p\exp\{x_{ik}'\beta_i\}} \]

\(x_i\) is \(n_i \times k\) and \(i = 1, \ldots, N\). There are \(p\) alternatives, \(j = 1, \ldots, p\). An outside option, often denoted \(j=0\) can be introduced by assigning \(0\)’s to that option’s covariate (\(x\)) values.

We impose sign constraints by defining a \(k\)-length constraint vector SignRes that takes values from the set \(\{-1, 0, 1\}\) to define \(\beta_{ik} = f(\beta_{ik}^*)\) where \(f(\cdot)\) is as follows:

\[ \beta_{ik} = f(\beta_{ik}^*) = \left\{ \begin{array}{lcl} \exp(\beta_{ik}^*) & \text{if} & \texttt{SignRes[k]} = 1 \\ \beta_{ik}^* & \text{if} & \texttt{SignRes[k]} = 0 \\ -\exp(\beta_{ik}^*) & \text{if} & \texttt{SignRes[k]} = -1 \\ \end{array} \right. \]

The “deep” individual-specific parameters (\(\beta_i^*\)) are assumed to be drawn from a mixture of \(M\) normal distributions with mean values driven by cross-sectional unit characteristics \(Z\). That is, \(\beta_i^* = z_i' \Delta + u_i\) where \(u_i\) has a mixture-of-normals distribution.1

Considering \(\beta_i^*\) a length-\(k\) row vector, we will stack the \(N\) \(\beta_i^*\)’s vertically and write:

\[ B=Z\Delta + U \] Thus we have \(\beta_i\), \(z_i\), and \(u_i\) as the \(i^\text{th}\) rows of \(B\), \(Z\), and \(U\). \(B\) is \(N \times k\), \(Z\) is \(N \times M\), \(\Delta\) is \(M \times k\), and \(U\) is \(N \times k\) where the distribution on \(U\) is such that:

\[ \Pr(\beta_{ik}^*) = \sum_{m=1}^M \pi_m \phi(z_i' \Delta \vert \mu_j, \Sigma_j) \]

\(\phi\) is the normal pdf.


Natural conjugate priors are specified:

\[ \pi \sim \text{Dirichlet}(a) \] \[ \text{vec}(\Delta) = \delta \sim MVN(\bar{\delta}, A_\delta^{-1}) \] \[ \mu_m \sim MVN(\bar{\mu}, \Sigma_m \otimes a_\mu^{-1}) \] \[ \Sigma_m \sim IW(\nu, V) \]

This specification of priors assumes that the \((\mu_m,\Sigma_m)\) are independent and that, conditional on the hyperparameters, the \(\beta_i\)’s are independent.

\(a\) implements prior beliefs on the number of normal components in the mixture with a default of 5. \(\nu\) is a “tightness” parameter of the inverted-Wishart distribution and \(V\) is its location matrix. Without sign constraints, they default to \(\nu=k+3\) and \(V=\nu I\), which has the effect of centering the prior on \(I\) and making it “barely proper”. \(a_\mu\) is a tightness parameter for the priors on \(\mu\), and when no sign constraints are imposed it defaults to an extremely diffuse prior of 0.01.

These defaults assume the logit coefficients (\(\beta_{ik}\)’s) are on the order of approximately 1 and, if so, are typically reasonable hyperprior values. However, when sign constraints are imposed, say, SignRes[k]=-1 such that \(\beta_{ik} = -\exp\{\beta_{ik}^*\}\), then these hyperprior defults pile up mass near zero — a result that follows from the nature of the exponential function and the fact that the \(\beta_{ik}^*\)’s are on the log scale. Let’s show this graphically.

# define function
drawprior <- function (mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw) {
  betakstar <- double(ndraw)
  betak     <- double(ndraw)
  otherbeta <- double(ndraw)
  mubar     <- c(rep(0, nvar-1), mubar_betak)
  for(i in 1:ndraw) {
    for(k in 1:ncomp) {
      Sigma <- rwishart(nu,chol2inv(chol(V)))$IW
      comps[[k]] <- list(mubar + t(chol(Sigma/Amu)) %*% rnorm(nvar), 
                         backsolve(chol(Sigma), diag(1,nvar)) )
    pvec         <- rdirichlet(a)
    beta         <- rmixture(1,pvec,comps)$x
    betakstar[i] <- beta[nvar]
    betak[i]     <- -exp(beta[nvar])
    otherbeta[i] <- beta[1]
  return(list(betakstar=betakstar, betak=betak, otherbeta=otherbeta))
# specify rhierMnlRwMixture defaults
mubar_betak <- 0
nvar  <- 10
ncomp <- 3
a     <- rep(5, ncomp)
nu    <- nvar + 3
Amu   <- 0.01
V     <- nu*diag(c(rep(1,nvar-1),1))
ndraw <- 10000
defaultprior <- drawprior(mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw)
# plot priors under defaults
trimhist <- -20
hist(defaultprior$betakstar, breaks=40, col="magenta", 
     main="Beta_k_star", xlab="", ylab="", yaxt="n")
     breaks=40, col="magenta", main="Beta_k",
     xlab="", ylab="", yaxt="n", xlim=c(trimhist,0))
hist(defaultprior$otherbeta, breaks=40, col="magenta",
     main="Other Beta", xlab="", ylab="", yaxt="n")