Recursive Forecast Combination for Dependent Heterogeneous Data

Sancetta, Alessio

doi:10.1017/s0266466609100105

Cited by 15 publications

(19 citation statements)

References 33 publications

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“…In addition, macroeconometric models are likely to be subject to structural breaks due to policy changes and shifts in tastes and technology. As Clements and Hendry (1998, 1999, 2006 emphasise, structural breaks are often the main source of forecast failure and represent the most serious form of model uncertainty.…”

Section: Forecasting With the Vecx*(22) Modelmentioning

confidence: 99%

Forecasting the Swiss Economy Using Vecx* Models: an Exercise in Forecast Combination Across Models and Observation Windows

Assenmacher

Pesaran

2008

National Institute Economic Review

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This paper uses vector error correction models of Switzerland for forecasting output, inflation and the short-term interest rate. It considers three different ways of dealing with forecast uncertainties. First, it investigates the effect on forecasting performance of averaging over forecasts from different models. Second, it considers averaging forecasts from different estimation windows. It is found that averaging over estimation windows is at least as effective as averaging over different models and both complement each other. Third, it examines whether using weighting schemes from the machine learning literature improves the average forecast. Compared to equal weights the effect of alternative weighting schemes on forecast accuracy is small in the present application.

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Section: Forecasting With the Vecx*(22) Modelmentioning

confidence: 99%

Forecasting the Swiss Economy Using Vecx* Models: an Exercise in Forecast Combination Across Models and Observation Windows

Assenmacher

Pesaran

2008

National Institute Economic Review

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“…The MLS method is implemented according to Algorithm 1 in Sancetta (2010). The core step in the algorithm is to compute the currentperiod weight (before shrinkage) where is the learning rate parameter, and is a parameter that controls the speed of learning.…”

Section: Measuring the Performances Of Combination Methodsmentioning

confidence: 99%

“…In addition to AFTER, we consider another on-line recursive algorithm from the machine learning literature with shrinking (henceforth MLS) due to Sancetta (2010). Unlike the BG approach, the on-line algorithms tend to select the few top forecasters, thus requiring much smaller number of estimated parameters.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning and Forecast Combination in Incomplete Panels

2013

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“…For example, Sancetta [17] assumed that the tails of the target variables are no heavier than exponential decays, which restricts the heaviness of the tails of the forecast errors. Wei & Yang [12] designed a method for errors heavier than the normal distributions, but not heavier than the double-exponential distributions.…”

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confidence: 99%

Forecast Combination under Heavy-Tailed Errors

2015

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Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our knowledge, little has been done to obtain reliable forecast combinations for such situations. The familiar forecast combination methods, such as simple average, least squares regression or those based on the variance-covariance of the forecasts, may perform very poorly due to the fact that outliers tend to occur, and they make these methods have unstable weights, leading to un-robust forecasts. To address this problem, in this paper, we propose two nonparametric forecast combination methods. One is specially proposed for the situations in which the forecast errors are strongly believed to have heavy tails that can be modeled by a scaled Student's t-distribution; the other is designed for relatively more general situations when there is a lack of strong or consistent evidence on the tail behaviors of the forecast errors due to a shortage of data and/or an evolving data-generating process. Adaptive risk bounds of both methods are developed. They show that the resulting combined forecasts yield near optimal mean forecast errors relative to the candidate forecasts. Simulations and a real example demonstrate their superior performance in that they indeed tend to have significantly smaller prediction errors than the previous combination methods in the presence of forecast outliers.

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Recursive Forecast Combination for Dependent Heterogeneous Data

Cited by 15 publications

References 33 publications

Forecasting the Swiss Economy Using Vecx* Models: an Exercise in Forecast Combination Across Models and Observation Windows

Forecasting the Swiss Economy Using Vecx* Models: an Exercise in Forecast Combination Across Models and Observation Windows

Machine Learning and Forecast Combination in Incomplete Panels

Forecast Combination under Heavy-Tailed Errors

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