LIEBERMANN, JOELLE (2012): Realtime forecasting in a datarich environment.

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Abstract
This paper assesses the ability of different models to forecast key real and nominal U.S. monthly macroeconomic variables in a datarich environment and from the perspective of a realtime forecaster, i.e. taking into account the realtime data revisions process and data flow. We find that for the real variables predictability is confined over the recent recession/crisis period. This is in line with the findings of D’Agostino and Giannone (2012) that gains in relative performance of models using large datasets over univariate models are driven by downturn periods which are characterized by higher comovements. Regarding inflation, results are stable across time, but predictability is mainly found at the very shortterm horizons. Inflation is known to be hard to forecast, but by exploiting timely information one obtains gains at nowcasting and forecasting onemonth ahead, especially with Bayesian VARs. Furthermore, for both real and nominal variables, the direct pooling of information using a high dimensional model (dynamic factor model or Bayesian VAR) which takes into account the crosscorrelation between the variables and efficiently deals with the “ragged edge”structure of the dataset, yields more accurate forecasts than the indirect pooling of bivariate forecasts/models.
Item Type:  MPRA Paper 

Original Title:  Realtime forecasting in a datarich environment 
Language:  English 
Keywords:  Realtime data, Nowcasting, Forecasting, Factor model, Bayesian VAR, Forecast pooling 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods E  Macroeconomics and Monetary Economics > E5  Monetary Policy, Central Banking, and the Supply of Money and Credit > E52  Monetary Policy C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C33  Panel Data Models ; Spatiotemporal Models C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General 
Item ID:  39452 
Depositing User:  JOELLE LIEBERMANN 
Date Deposited:  14. Jun 2012 16:18 
Last Modified:  24. Mar 2015 22:03 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/39452 