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Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility


This paper develops a method for producing current-quarter forecasts of GDP growth with a (possibly large) range of available within-the-quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and interest rates. In light of existing evidence of time variation in the variances of shocks to GDP, we consider versions of the model with both constant variances and stochastic volatility. We also evaluate models with either constant or time-varying regression coefficients. We use Bayesian methods to estimate the model, in order to facilitate providing shrinkage on the (possibly large) set of model parameters and conveniently generate predictive densities. We provide results on the accuracy of nowcasts of real-time GDP growth in the U.S. from 1985 through 2011. In terms of point forecasts, our proposal is comparable to alternative econometric methods and survey forecasts. In addition, it provides reliable density forecasts, for which the stochastic volatility specification is quite useful, while parameter time-variation does not seem to matter.

Keywords: Prediction, forecasting, Bayesian methods, mixed frequency models.

JEL classification: E37, C53, C22.


Suggested citation: Carriero, Andrea, Todd E. Clark, and Massimiliano Marcellino, 2012. “Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility,” Federal Reserve Bank of Cleveland Working Paper No. 12-27

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