Federal Reserve Bank of New York
Exploiting the monthly data flow in structural forecasting
This paper develops a framework that allows us to combine the tools provided by structural models for economic interpretation and policy analysis with those of reduced-form models designed for nowcasting. We show how to map a quarterly dynamic stochastic general equilibrium (DSGE) model into a higher frequency (monthly) version that maintains the same economic restrictions. Moreover, we show how to augment the monthly DSGE with auxiliary data that can enhance the analysis and the predictive accuracy in now-casting and forecasting. Our empirical results show that both the monthly version of the DSGE and the auxiliary variables offer help in real time for identifying the drivers of the dynamics of the economy.
Cite this item
Domenico Giannone & Francesca Monti & Lucrezia Reichlin, Exploiting the monthly data flow in structural forecasting, Federal Reserve Bank of New York, Staff Reports 751, 01 Dec 2015.
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
Keywords: DSGE models; forecasting; temporal aggregation; mixed-frequency data; large data sets
This item with handle RePEc:fip:fednsr:751
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