Time-varying intercepts and equilibrium analysis: an extension of the dynamic almost ideal demand model
Demographic effects and user costs in demand systems have usually been modelled explicitly. A more robust approach is a state space formulation of the demand system, where time-varying intercepts account for the effects of unobservable variables. The author embeds such a system in a vector autoregressive distributed lag model, with a Bayesian hierarchical prior. The model is estimated by a Markov chain Monte Carlo method on samples involving quarterly US and UK data. In the US case, the results are compared with a previously published cointegration analysis of the same data. Copyright © 2002 John Wiley & Sons, Ltd.
Volume (Year): 18 (2003)
Issue (Month): 2 ()
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