Forecast combinations under structural break uncertainty

Tian, Jing; Anderson, Heather M.

doi:10.1016/j.ijforecast.2013.06.003

Cited by 32 publications

(26 citation statements)

References 16 publications

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“…Because LW is better suited for the situation of late breaks or large break sizes (Tian and Anderson, 2014), the results collectively indicate that the relationships between future equity premiums and economic predictors experience either early and/or small breaks. significantly greater than zero.…”

Section: Out-of-sample Forecasting Resultsmentioning

confidence: 82%

“…This idea is consistent with the Robust Optimal Weights on Observations method. Tian and Anderson (2014) propose an alternative simple weighting method that imposes more weights on recent observations than the equal weighting method. These weights are proportional to the location of the starting date of each estimation window (i.e., τ); they identify combination forecasts as location-weighted forecasts as follows:…”

Section: Forecast Combination Across Estimation Windowsmentioning

confidence: 99%

“…The location-weighted forecasts are superior to the equally weighted forecasts when the (standardized) break sizes are large and the last break date is close to the end of the entire sample (Tian and Anderson, 2014). [Insert Table 2 here.…”

Section: Forecast Combination Across Estimation Windowsmentioning

confidence: 99%

“…To examine the best methods to manage SBU and to determine whether the out-of-sample performance of equity premium predictive regression models can be improved by incorporating SBU, we discuss and compare five alternative methods that deal with SBU: the expanding window, the rolling window, the Bai-Perron method Perron, 2003, 1998), Robust Optimal Weights on Observations (Pesaran et al, 2013), and Forecast Combination across Estimation Windows (Pesaran and Timmermann, 2007;Tian and Anderson, 2014). We examine the performances of these methods for a broad range of bivariate prediction models that use common predictors in the literature, including 14 economic predictors (see Welch and Goyal, 2008;Rapach et al, 2010) and 14 indicators based on technical trading rules (see Neely et al, 2014), in addition to two "kitchen sink" prediction models based on these economic and technical predictors.…”

Section: Introductionmentioning

confidence: 99%

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Improving equity premium forecasts by incorporating structural break uncertainty

Tian

Zhou

2016

Accounting & Finance

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This paper compares five alternative methods for directly dealing with structural break uncertainty in forecasting the U.S. equity premium using 30 widely used bivariate and multivariate predictive regressions. We find that two recently developed methods -Robust Optimal Weights on Observations and Forecast Combination across Estimation Windows -outperform the conventional rolling window and post-break estimation methods. This result indicates that very early historical information is beneficial for U.S. equity premium forecasting but should be discounted to incorporate structural break uncertainty.

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Section: Out-of-sample Forecasting Resultsmentioning

confidence: 82%

Section: Forecast Combination Across Estimation Windowsmentioning

confidence: 99%

Section: Forecast Combination Across Estimation Windowsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving equity premium forecasts by incorporating structural break uncertainty

Tian

Zhou

2016

Accounting & Finance

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“…In this context, Pesaran and Timmermann [12] have proposed forecast combinations formed by averaging across forecasts generated by using all possible window sizes subject to a minimum length requirement. Based on the same idea, more complex forecasting schemes have been proposed (see, for example, [34,38]). …”

Section: Forecasting Methods In the Presence Of Structural Breaksmentioning

confidence: 99%

Forecast Combinations in the Presence of Structural Breaks: Evidence from U.S. Equity Markets

Gaetano

2018

Mathematics

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Realized volatility, building on the theory of a simple continuous time process, has recently received attention as a nonparametric ex-post estimate of the return variation. This paper addresses the problem of parameter instability due to the presence of structural breaks in realized volatility in the context of three HAR-type models. The analysis is conducted on four major U.S. equity indices. More specifically, a recursive testing methodology is performed to evaluate the null hypothesis of constant parameters, and then, the performance of several forecast combinations based on different weighting schemes is compared in an out-of-sample variance forecasting exercise. The main findings are the following: (i) the hypothesis of constant model parameters is rejected for all markets under consideration; (ii) in all cases, the recursive forecasting approach, which is appropriate in the absence of structural changes, is outperformed by forecast combination schemes; and (iii) weighting schemes that assign more weight in most recent observations are superior in the majority of cases.

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Combination of Forecasts across Estimation Windows: An Application to Air Travel Demand

Jungmittag

2016

Journal of Forecasting

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This paper applies combining forecasts of air travel demand generated from the same model but over different estimation windows. The combination approach used resorts to Pesaran and Pick (2011), but the empirical application is extended in several ways. The forecasts are based on a seasonal Box-Jenkins model (SARIMA), which is adequate to forecast monthly air travel demand with distinct seasonal patterns at the largest German airport Frankfurt am Main. Furthermore, forecasts with forecast horizons from one to twelve months-ahead, which are based on different average estimation windows, expanding windows and single rolling windows, are compared with baseline forecasts based on an expanding window of the observations after a structural break. The forecast exercise shows that the average window forecasts mostly outperform the alternative single window forecasts.

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Forecast combinations under structural break uncertainty

Cited by 32 publications

References 16 publications

Improving equity premium forecasts by incorporating structural break uncertainty

Improving equity premium forecasts by incorporating structural break uncertainty

Forecast Combinations in the Presence of Structural Breaks: Evidence from U.S. Equity Markets

Combination of Forecasts across Estimation Windows: An Application to Air Travel Demand

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