Nowcasting the Unemployment Rate in the EU with Seasonal BVAR and Google Search Data - Jetro Anttonen (The Research Institute of the Finnish Economy (ETLA), 2018)
In this paper a Bayesian vector autoregressive model for nowcasting the seasonally non-adjusted unemployment rate in EU-countries is developed. On top of the official statistical releases, the model utilizes Google search data and the effect of Google data on the forecasting performance of the model is assessed. The Google data is found to yield modest improvements in forecasting accuracy of the model. To the author's knowledge, this is the first time the forecasting performance of the Google search data has been studied in the context of Bayesian vector autoregressive model. This paper also adds to the empirical literature on the hyperparameter choice with Bayesian vector autoregressive models. The hyperparameters are set according to the mode of the posterior distribution of the hyperparameters, and this is found to improve the out-of-sample forecasting accuracy of the model significantly, compared to the rule-of-thumb values often used in the literature.
Keywords: C32; C53; C55; C82; E27; 330; Nowcasting; Forecasting; BVAR; Big Data; Unemployment