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Bayesian Model 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))

Abstract

Bayesian model averaging (BMA) is a statistical method to rigorously take model uncertainty into account. This chapter gives a coherent overview on the statistical foundations and methods of BMA and its usefulness for forecasting, but also for the identification of robust determinants. The focus is given on economic applications. We describe the BMA framework in the context of linear models. Different model and parameter priors are discussed in detail and suitable inference methods and posterior analysis tools are presented. We illustrate the use of the presented BMA framework to study potential drivers of box office revenues and to forecast these revenues. The available data set does not only contain traditional variables such as budget or genre categorization, but also variables derived from social media content which capture sentiment and volume of Twitter messages. We discuss the impact of different model specifications and describe how results are obtained using the open-source package BMS available for the R environment for statistical computing and graphics.

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Notes

  1. 1.

    This working paper was published in a slightly different version as Doppelhofer and Weeks (2009a).

  2. 2.

    In the context of economic growth, this is in line with the model formulation in Durlauf, Kourtellos, and Tan (2008) where competing groups of explanatory variables emerging from different theories are assumed to be relevant.

  3. 3.

    Following Lehrer and Xie (2017), this sample selection criterion was proposed by IHS film consulting and accounts for 41.4% of released movies.

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Authors and Affiliations

  1. Salzburg Centre of European Union Studies (SCEUS), Department of Business, Economics and Social Theory, Paris Lodron University of Salzburg, Salzburg, Austria

    Paul Hofmarcher

  2. Department of Applied Statistics, Johannes Kepler University Linz, Linz, Austria

    Bettina Grün

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  1. Paul Hofmarcher
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  2. Bettina Grün
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Correspondence to Paul Hofmarcher .

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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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Hofmarcher, P., Grün, B. (2020). Bayesian Model 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_12

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