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Bayesian inference on time-varying proportions

In: Bayesian Econometrics

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  • William J. McCausland
  • Brahim Lgui

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.

Suggested Citation

  • William J. McCausland & Brahim Lgui, 2008. "Bayesian inference on time-varying proportions," Advances in Econometrics, in: Bayesian Econometrics, pages 525-544, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-9053(08)23016-1
    DOI: 10.1016/S0731-9053(08)23016-1
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