11/27/2023 0 Comments Google trends data time series![]() Search terms with high inclusion probability have good economic interpretation, reflecting leading signals of economic anxiety and wealth effects. 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 results are normalized to the time and location of a query by the following process: Each data point is divided by the total searches of the geography. 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. Compared to other types of models, time-series forecasting comes with its unique challenges, such as seasonality, holiday effects, data sparsity, and changing trends. 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. We augment and enhance both model and methodology to make these better amenable to nowcasting with large number of potential covariates. ![]() ![]() We apply the method to nowcast US quarterly. GDP growth in real time through the lens of mixed frequency Bayesian Structural Time Series (BSTS) models. We propose a flexible and interpretable nowcasting method for macroeconomic time series using high frequency data. To solve this problem, we have developed a method called Google Trends Anchor Bank. This paper investigates the benefits of internet search data in the form of Google Trends for nowcasting real U.S. De-normalizing Google Trends data can be very useful, but it is tricky, due to rounding errors: when comparing 2 queries of vastly different search volume, the time series for the less frequent query could appear to be 0 everywhere. The Bayesian Structural Time Series (BSTS) model, as proposed by Scott and Varian (2014), provides a conceptually attractive model for nowcasting aggregate economic time-series with heterogeneous data sources, as it flexibly estimates latent time-trends, seasonality and deviations or ‘irregular’ dynamics through variable selection using a high-d.
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