Persistence in forecasting performance and conditional combination strategies

By Marco Aiolfi, Allan G. Timmermann. 2006

344 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/01c3525a4c08183be00f588adc8fbdc290ebc1be

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Citations in the corpus

Combination of long term and short term forecasts, with application to tourism demand forecasting (2011, methodology): “Then, assuming that the weights sum to 1, the optimal weights can be obtained as (see Timmermann , 2006, for the derivation): w 1 = σ 2 2 − σ 12 σ 2 1 + σ 2 2 − 2σ 12 (2) w 2 = 1 − w 1 .”
Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition (2011, background): “In addition, Aiolfi and Timmermann (2006) found evidence of persistence in the out-of-sample performances of both linear and nonlinear forecasting models.”
Nowcasting GDP in Real Time: A Density Combination Approach (2014, positive): “Our approach is close to Aiolfi and Timmermann (2006) in the sense that we combine forecasts in more than one step.”
Meta-learning for time series forecasting and forecast combination (2010, methodology): “• In variance-based pooling , past performance is used to group forecasts into two or three clusters by a k-means algorithm as suggested by Aiolﬁ and Timmermann in [3].”
Combining expert forecasts: Can anything beat the simple average? (2013, methodology): “We therefore follow the k-mean clustering approach of Aiolfi and Timmermann (2006) and replace the N individual forecasts in equation (2) with the mean forecast computed for a small number of clusters. We consider two or three clusters (c= 2 or c = 3 and base the clustering on the most recently obse”
Forecasts of US Short-Term Interest Rates: A Flexible Forecast Combination Approach (2005, methodology): “…the forecast combination literature by proposing a flexible combination approach that extends earlier work in the combination literature by Bates and Granger (1969), Diebold and Pauly (1987), Deutsch, Granger and Terasvirta (1994), Stock and Watson (2001, 2005) and Aiolfi and Timmermann (2006).”
Is there an optimal forecast combination (2014, methodology): “Aiol and Timmermann (2006) suggest to sort forecasts into quartiles based on their historical forecasting performance up to the point of prediction, say s.”
Forecasting with Many Predictors (2006, background): “and Watson (2003, 2004a), Kitchen and Monaco (2003), and Aiolfi and Timmermann (2004). The studies by Figlewski (1983) and Figlewski and Urich (1983) use static factor models for forecast combining; they found that the factor model forecasts improved equal-weighted averages in one instance (n = 33 p”