Nowcasting in Real Time Using Popularity Priors

Monokroussos, George (2015): Nowcasting in Real Time Using Popularity Priors.

Abstract

This paper proposes a Bayesian nowcasting approach that utilizes information coming both from large real-time data sets and from priors constructed using internet search popularity measures. Exploiting rich information sets has been shown to deliver significant gains in nowcasting contexts, whereas popularity priors can lead to better nowcasts in the face of model and data uncertainty in real time, challenges which can be particularly relevant during turning points. It is shown, for a period centered on the latest recession in the United States, that this approach has the potential to deliver particularly good real-time nowcasts of GDP growth.

• Item Type: MPRA Paper
• Language: English
• Keywords: Nowcasting, Gibbs Sampling, Factor Models, Kalman Filter, Real-Time Data, Google Trends, Monetary Policy, Great Recession.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy

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