Stochastic search variable selection in vector error correction models with an application to a model of the UK macroeconomy
开发了适用于向量误差修正模型的随机搜索变量选择方法，允许研究者从单一无约束模型出发自动进行模型选择或模型平均，并应用于英国宏观经济模型。
SUMMARY This paper develops methods for stochastic search variable selection (currently popular with regression and vector autoregressive models) for vector error correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic model. Copyright © 2011 John Wiley & Sons, Ltd.