TY - CHAP
AB - Time-varying proportions arise frequently in economics. Market shares show the relative importance of firms in a market. Labor economists divide populations into different labor market segments. Expenditure shares describe how consumers and firms allocate total expenditure to various categories. We introduce a state space model where unobserved states are Gaussian and observations are conditionally Dirichlet. Markov chain Monte Carlo techniques allow inference for unknown parameters and states. We draw states as a block using a multivariate Gaussian proposal distribution based on a quadratic approximation of the log conditional density of states given parameters and data. Repeated draws from the proposal distribution are particularly efficient. We illustrate using automobile production data.
AU - McCausland, WJ
AU - Lgui, B
CY - USA
DA - 2009/01/01
DO - 10.1016/S0731-9053(08)23016-1
ED - 1
EP - 544
JO - Bayesian Econometrics (Advances in Econometrics Vol 23)
PB - Emerald
PY - 2009/01/01
SP - 525
TI - Bayesian inference on time-varying proportions
Y1 - 2009/01/01
Y2 - 2020/06/01
ER -