Forecast Combination with Entry and Exit of Experts

Capistrán, Carlos; Timmermann, Allan

doi:10.2139/ssrn.1270549

Cited by 39 publications

(54 citation statements)

References 23 publications

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“…This missing data issue creates a major challenge for forecast combination in the ECB-SPF data. See Genre et al (2013) for further details, and Capistrán and Timmermann (2009) on missingness in the US SPF data, for which the situation is similar. These data properties motivate a number of simple yet principled combination methods which we describe in Sect.…”

Section: Data On Forecasts and Realizationsmentioning

confidence: 94%

Survey-based forecast distributions for Euro Area growth and inflation: ensembles versus histograms

Krüger

2017

Empir Econ

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Ensemble methods can be used to construct a forecast distribution from a collection of point forecasts. They are used extensively in meteorology, but have received little direct attention in economics. In a real-time analysis of the ECB's Survey of Professional Forecasters, we compare ensemble methods to histogrambased forecast distributions of GDP growth and inflation in the Euro Area. We find that ensembles perform very similarly to histograms, while being simpler to handle in practice. Given the wide availability of surveys that collect point forecasts but not histograms, these results suggest that ensembles deserve further investigation in economics.

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Section: Data On Forecasts and Realizationsmentioning

confidence: 94%

Survey-based forecast distributions for Euro Area growth and inflation: ensembles versus histograms

Krüger

2017

Empir Econ

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“…Capistrán and Timmermann (2009) provide support for the use of the median; they show that means of survey forecasts performs better than other methods of individual forecast combination when evaluated by root-mean-squared errors. In the case of SPF, the mean and median forecasts are very close to each other, as documented in Croushore (2010).…”

Section: Uncertainty Based On Different Measures Of Central Tendencmentioning

confidence: 77%

Macroeconomic Uncertainty Through the Lens of Professional Forecasters

Sekkel

2017

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We analyze the evolution of macroeconomic uncertainty in the United States, based on the forecast errors of consensus survey forecasts of various economic indicators. Comprehensive information contained in the survey forecasts enables us to capture a real-time subjective measure of uncertainty in a simple framework. We jointly model and estimate macroeconomic (common) and indicator-specific uncertainties of four indicators, using a factor stochastic volatility model. Our macroeconomic uncertainty has three major spikes aligned with the 1973-75, 1980, and 2007-09 recessions, while other recessions were characterized by increases in indicator-specific uncertainties. We also show that the selection of data vintages affects the estimates and relative size of jumps in estimated uncertainty series. Finally, our macroeconomic uncertainty has a persistent negative impact on real economic activity, rather than producing "wait-and-see" dynamics.

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“…Bayesian model averaging (Lancaster, 2004, p. 101) has gained popularity due to its natural interpretation and good performance in practice (see Raftery et al (1997), andHoeting et al (1999) for example). Recently, important specific issues in forecast combination have been investigated such as Hubrich (2011), Pesaran andPick (2010) and Capistrán andTimmermann (2009). Timmermann (2006) provide an extensive survey of the literature and list the advantages one can expect from pooling forecasts.…”

Section: Forecast Combinationmentioning

confidence: 99%

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

Pauwels

Vasnev

2016

Empir Econ

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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 and quadratic-scores are both 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 the US Federal Open Market Committee (FOMC) decisions in changing the federal funds target rate. Several of the economic fundamentals influencing the FOMC decisions are nonstationary over time 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. 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 and quadratic-scores are both 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 the US Federal Open Market Committee (FOMC) decisions in changing the federal funds target rate. Several of the economic fundamentals influencing the FOMC decisions are nonstationary over time 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.

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Forecast Combination with Entry and Exit of Experts

Cited by 39 publications

References 23 publications

Survey-based forecast distributions for Euro Area growth and inflation: ensembles versus histograms

Survey-based forecast distributions for Euro Area growth and inflation: ensembles versus histograms

Macroeconomic Uncertainty Through the Lens of Professional Forecasters

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

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