Forecast combination for discrete choice models: predicting FOMC monetary policy decisions

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• 作者：Laurent L. Pauwels ; Andrey L. Vasnev
• 关键词：Forecast combination ; Probability forecast ; Discrete choice models ; Monetary policy decisions ; Real ; time data
• 刊名：Empirical Economics
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：52
• 期：1
• 页码：229-254
• 全文大小：
• 刊物主题：Econometrics; Statistics for Business/Economics/Mathematical Finance/Insurance; Economic Theory/Quantitative Economics/Mathematical Methods;
• 出版者：Springer Berlin Heidelberg
• ISSN：1435-8921
• 卷排序：52

文摘

This paper provides a methodology for combining forecasts based on several discrete choice models. This is achieved primarily by combining one-step-ahead probability forecasts associated with each model. The paper applies well-established scoring rules for qualitative response models in the context of forecast combination. Log scores, quadratic scores and Epstein scores are used to evaluate the forecasting accuracy of each model and to combine the probability forecasts. In addition to producing point forecasts, the effect of sampling variation is also assessed. This methodology is applied to forecast US Federal Open Market Committee (FOMC) decisions regarding changes in the federal funds target rate. Several of the economic fundamentals influencing the FOMC’s decisions are integrated, or I(1), and are modeled in a similar fashion to Hu and Phillips (J Appl Econom 19(7):851– 867, 2004). The empirical results show that combining forecasted probabilities using scores generally outperforms both equal weight combination and forecasts based on multivariate models.