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- W1641644492 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." @default.
- W1641644492 created "2016-06-24" @default.
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- W1641644492 date "2012-01-01" @default.
- W1641644492 modified "2023-10-16" @default.
- W1641644492 title "Forecast Combination for Discrete Choice Models: Predicting FOMC Monetary Policy Decisions" @default.
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- W1641644492 doi "https://doi.org/10.2139/ssrn.2203549" @default.
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