文章基本信息

标题：Real-time Prediction with UK Monetary Aggregates in the Presence of Model Uncertainty
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作者：Anthony Garratt ; Gary Koop ; Emi Mise 等
期刊名称：Birkbeck Working Papers in Economics and Finance / School of Economics, Mathematics and Statistics, Birkbeck College
印刷版ISSN：1745-8587
出版年度：2007
卷号：2007
出版社：London University
摘要：A popular account for the demise of the UK monetary targeting regime in the 1980s blames the weak predictive relationships between broad money and inflation and real output. In this paper, we investigate these relationships using a variety of monetary aggregates which were used as intermediate UK policy targets. We use both real-time and final vintage data and consider a large set of recursively estimated Vector Autoregressive (VAR) and Vector Error Correction models (VECM). These models differ in terms of lag length and the number of cointegrating relationships. Faced with this model uncertainty, we utilize Bayesian model averaging (BMA) and contrast it with a strategy of selecting a single best model. Using the real-time data available to UK policymakers at the time, we demonstrate that the in-sample predictive content of broad money fluctuates throughout the 1980s for both strategies. However, the strategy of choosing a single best model amplifies these fluctuations. Out-of-sample predictive evaluations rarely suggest that money matters for either inflation or real output, regardless of whether we select a single model or do BMA. Overall, we conclude that the money was a weak (and unreliable) predictor for these key macroeconomic variables. But the view that the predictive content of UK broad money diminished during the 1980s receives little support using either the real-time or final vintage data.