Real-Time Forecasting with a Mixed-Frequency VAR
50 Pages Posted: 10 Dec 2013 Last revised: 13 May 2023
Date Written: December 2013
Abstract
This paper develops a vector autoregression (VAR) for time series which are observed at mixed frequencies - quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time data set, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly-frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time.
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting
By Mario Forni, Marc Hallin, ...
-
Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
-
By James H. Stock and Mark W. Watson
-
Monetary Policy in a Data-Rich Environment
By Ben S. Bernanke and Jean Boivin
-
Eurocoin: A Real Time Coincident Indicator of the Euro Area Business Cycle
By Filippo Altissimo, Antonio Bassanetti, ...
-
Are More Data Always Better for Factor Analysis?
By Jean Boivin and Serena Ng
-
Implications of Dynamic Factor Models for VAR Analysis
By James H. Stock and Mark W. Watson
-
By Domenico Giannone, Lucrezia Reichlin, ...
-
By Domenico Giannone, Lucrezia Reichlin, ...