Forecast Combination for Discrete Choice Models: Predicting FOMC Monetary Policy Decisions

30 Pages Posted: 19 Jan 2013

See all articles by Laurent L. Pauwels

Laurent L. Pauwels

NYU Abu Dhabi; The University of Sydney; Australian National University (ANU) - Centre for Applied Macroeconomic Analysis (CAMA)

Andrey L. Vasnev

University of Sydney

Date Written: May 2012

Abstract

This paper provides a methodology for combining forecasts based on several discrete choice models. This is achieved primarily by combining one-step-ahead probability forecast 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 forecast- ing 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 the US Federal Open Market Committee (FOMC) decisions in changing the federal funds target rate. Several of the economic funda- mentals influencing the FOMC decisions are integrated, or I(1), and are modelled in a similar fashion to Hu and Phillips (2004a, JoE). The empirical results show that combining forecasted probabilities using scores mostly outperforms both equal weight combination and forecasts based on multivariate models.

Keywords: forecast combination, probability forecast, discrete choice models, monetary policy decisions

Suggested Citation

Pauwels, Laurent L. and Vasnev, Andrey L., Forecast Combination for Discrete Choice Models: Predicting FOMC Monetary Policy Decisions (May 2012). Available at SSRN: https://ssrn.com/abstract=2203549 or http://dx.doi.org/10.2139/ssrn.2203549

Laurent L. Pauwels

NYU Abu Dhabi ( email )

PO Box 129188
Abu Dhabi
United Arab Emirates

The University of Sydney ( email )

University of Sydney
Sydney, NSW 2006
Australia

Australian National University (ANU) - Centre for Applied Macroeconomic Analysis (CAMA) ( email )

ANU College of Business and Economics
Canberra, Australian Capital Territory 0200
Australia

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