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Developments on the Bayesian Structural Time Series Model: Trending Growth

David Kohns () and Arnab Bhattacharjee ()

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Abstract: This paper investigates the added benefit of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of the mixed frequency augmented Bayesian Structural Time Series model (BSTS) of Scott and Varian (2014). We show that a large dimensional set of search terms are able to improve nowcasts before other macro data becomes available early on the quarter. Search terms with high inclusion probability have negative correlation with GDP growth, which we reason to stem from them signalling special attention likely due to expected large troughs. We further offer several improvements on the priors: we allow to shrink state variances to zero to avoid overfitting states, extend the SSVS prior to the more flexible normal-inverse-gamma prior of Ishwaran et al. (2005) which stays agnostic about the underlying model size, as well as adapt the horseshoe prior of Carvalho et al. (2010) to the BSTS. The application to nowcasting GDP growth as well as a simulation study show that the horseshoe prior BSTS improves markedly over the SSVS and the original BSTS model, with largest gains to be expected in dense data-generating-processes.

Date: 2020-11
New Economics Papers: this item is included in nep-ets and nep-ict
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