Bayesian inference on time-varying proportions

William McCausland () and Brahim Lgui

A chapter in Bayesian Econometrics, 2008, pp 525-544 from Emerald Group Publishing Limited

Abstract: 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.

Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-9053(08)23016-1

DOI: 10.1016/S0731-9053(08)23016-1

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