Time‐varying intercepts and equilibrium analysis: an extension of the dynamic almost ideal demand model
提出一种状态空间形式的动态几乎理想需求模型，用时变截距处理不可观测变量影响，并用贝叶斯MCMC方法估计，基于美国和英国季度数据验证。
Abstract 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.