Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility

Nowcasting has come to be commonly viewed as an important and unique forecasting problem; see, for example, Banbura et al. (2011, 2013). It is important because current quarter forecasts of gross domestic product (GDP) growth and inflation provide useful summaries of recent news on the economy and because these forecasts are commonly used as inputs to forecasting models, such as some of the dynamic stochastic general equilibrium models in use at central banks, that are effective in medium-term forecasting but not necessarily short-term forecasting. As studies such as Faust and Wright (2009, 2013) have emphasized, initial quarter forecasts often play a key role in the accuracy of forecasts at subsequent horizons. Nowcasting is unique in that, to some degree, it involves ‘simply’ adding up information in data releases for the current quarter. A key challenge is dealing with the differences in data release dates that cause the available