|CV - Job Market Paper|
Job market candidate
C. Ramon Trias Fargas, 25-27
Available for Interviews at :
Simposio de la Asociación Española de Economía (SAEe), December 15-17, Bilbao, Spain
Allied Social Science Associations (ASSA), January 6-8, Chicago, US
Econometrics, Macroeconomics, Forecasting
"Optimal Density Forecast Combinations" (Job Market Paper)
How should researchers combine predictive densities to improve their forecasts? I propose consistent estimators of weights that deliver density forecast combinations approximating the true predictive density, conditional on the researcher's information set. Monte Carlo simulations confirm that the proposed methods work well for sample sizes of practical interest. An empirical example of forecasting monthly US industrial production demonstrates that the estimator delivers density forecasts which are superior to well-known benchmarks, such as the equal weights scheme. Specifically, I show that housing permits had valuable predictive power before and after the Great Recession. Furthermore, stock returns and corporate bond spreads proved to be useful predictors during the recent crisis, suggesting that financial variables help with density forecasting in a highly leveraged economy.
Non-technical summary of the paper: BarcelonaGSE The Voice
"Forecasting with DSGE Versus Reduced-Form Models: A Time-Variation Perspective"
"Confidence Intervals for the Strength of Identification", with Atsushi Inoue and Barbara Rossi
This paper provides a grid-bootstrap method to construct confidence intervals for the strength of identification in instrumental variable models. Monte Carlo simulations show that the method has good small sample size and proper properties. An empirical investigation of the New Keynesian Phillips Curve shows that weak identification is a concern even if one uses a factor model to summarize all the relevant information from a large number of instruments.