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Frequentist Averaging

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Macroeconomic Forecasting in the Era of Big Data

Part of the book series: Advanced Studies in Theoretical and Applied Econometrics ((ASTA,volume 52))

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Abstract

This chapter summarises the recent approaches to optimal forecast combination from a frequentist perspective. The availability of big data leads to the development of many different models of the same macroeconomic variables. The challenge is to seek the best way to combine all relevant information from big data to create optimal forecast. Forecast combination provides one plausible approach. This chapter discusses the practical aspects of combining forecasts optimally and theoretical properties of the combination both for point forecasts and density forecasts. Specifically, the chapter derives the asymptotic distributions of the estimated optimal weight under two of the most popular forecasting criteria: Mean Squared Forecast Error and Mean Absolute Deviation. This chapter also revisits the insights of the so-called forecast combination puzzle, which shows that in practice a simple average of forecasts outperforms more complex weighting strategies. These theoretical results help address the puzzle by providing a mean to test statistically the difference between the estimated optimal weight and the simple average. The optimal weights obtained from minimising the Kullback–Leibler Information Criterion (KLIC) are discussed in the context of density forecast combination. This chapter also proposes a novel Generalized Method of Moments approach for density forecast combination. The connection between the proposed approach and the conventional approach by minimising KLIC is also investigated in some details.

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Notes

  1. 1.

    Strictly speaking, some elements in β can be 0 but variables selection is not being conducted on x t.

  2. 2.

    More specifically, the proposition shows that the additional information from combining different densities must be greater than N nit.

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Author information

Authors and Affiliations

  1. School of Economics, Finance and Property, Curtin University, Perth, WA, Australia

    Felix Chan & Sylvia Soltyk

  2. Discipline of Business Analytics, University of Sydney, Sydney, NSW, Australia

    Laurent Pauwels

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  1. Felix Chan
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  2. Laurent Pauwels
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Correspondence to Felix Chan .

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Editors and Affiliations

  1. UHERO and Department of Economics, University of Hawaii at Manoa, Honolulu, HI, USA

    Peter Fuleky

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Chan, F., Pauwels, L., Soltyk, S. (2020). Frequentist Averaging. In: Fuleky, P. (eds) Macroeconomic Forecasting in the Era of Big Data. Advanced Studies in Theoretical and Applied Econometrics, vol 52. Springer, Cham. https://doi.org/10.1007/978-3-030-31150-6_11

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