David Kohns, Arnab Bhattacharjee
arXiv 2 Nov 2020 · Econometrics · publishedInternational Journal of Forecasting (2022) · 28 citations (OpenAlex)
arXiv:2011.00938 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the benefits of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of mixed frequency Bayesian Structural Time Series (BSTS) models. We augment and enhance both model and methodology to make these better amenable to nowcasting with large number of potential covariates. Specifically, we allow shrinking state variances towards zero to avoid overfitting, extend the SSVS (spike and slab variable selection) prior to the more flexible normal-inverse-gamma prior which stays agnostic about the underlying model size, as well as adapt the horseshoe prior to the BSTS. The application to nowcasting GDP growth as well as a simulation study demonstrate that the horseshoe prior BSTS improves markedly upon the SSVS and the original BSTS model with the largest gains in dense data-generating-processes. Our application also shows that a large dimensional set of search terms is able to improve nowcasts early in a specific quarter before other macroeconomic data become available. Search terms with high inclusion probability have good economic interpretation, reflecting leading signals of economic anxiety and wealth effects.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Woloszko, N (2020) Tracking activity in real time with google trends | 1.000 | 8 | 3 | 100% |
| 2 | Scott, S. L. and H. R. Varian (2014) Predicting the present with bayesian structural time series | 0.981 | 18 | 7 | 94% |
| 3 | Cross, J. L., C. Hou, and A. Poon (2020) Macroeconomic forecasting with large bayesian vars: Global-local priors and the illusion of sparsity | 0.928 | 4 | 3 | 100% |
| 4 | Giannone, D., M. Lenza, and G. E. Primiceri (2021) Economic predictions with big data: The illusion of sparsity | 0.843 | 3 | 3 | 100% |
| 5 | Carriero, A., T. E. Clark, and M. Marcellino (2015) Realtime nowcasting with a bayesian mixed frequency model with stochastic volatility | 0.737 | 3 | 2 | 100% |
| 6 | Piironen, J., A. Vehtari, et al (2017) Sparsity information and regularization in the horseshoe and other shrinkage priors | 0.693 | 6 | 2 | 50% |
| 7 | Grant, A. L. and J. C. Chan (2017) A bayesian model comparison for trend-cycle decompositions of output | 0.644 | 3 | 2 | 67% |
| 8 | Ishwaran, H., J. S. Rao, et al (2005) Spike and slab variable selection: frequentist and bayesian strategies | 0.644 | 3 | 2 | 67% |
| 9 | Carvalho, C. M., N. G. Polson, and J. G. Scott (2010) The horseshoe estimator for sparse signals | 0.644 | 2 | 2 | 100% |
| 10 | Askitas, N. and K. F. Zimmermann (2009) Google econometrics and unemployment forecasting | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 86 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | The ARR2 prior: flexible predictive prior definition for Bayesian auto-regressions | 0.405 | 1 | 1 |