Authors: Marco Del Negro, Raiden B. Hasegawa, and Frank Schorfheide
-
-
The New York Innovation Center bridges the worlds of finance, technology, and innovation and generates insights into high-value central bank-related opportunities.
Do you have a request for information and records? Learn how to submit it.
Learn about the history of the New York Fed and central banking in the United States through articles, speeches, photos and video.
-
-
-
The Governance & Culture Reform hub is designed to foster discussion about corporate governance and the reform of culture and behavior in the financial services industry.
Need to file a report with the New York Fed? Here are all of the forms, instructions and other information related to regulatory and statistical reporting in one spot.
The New York Fed works to protect consumers as well as provides information and resources on how to avoid and report specific scams.
-
-
-
The New York Innovation Center bridges the worlds of finance, technology, and innovation and generates insights into high-value central bank-related opportunities.
The growing role of nonbank financial institutions, or NBFIs, in U.S. financial markets is a transformational trend with implications for monetary policy and financial stability.
The New York Fed offers the Central Banking Seminar and several specialized courses for central bankers and financial supervisors.
-
-
Staff Reports
Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance
October
2014 Number 695
JEL classification: C53, E31, E32, E37
We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call dynamic pools, and use it to investigate the relative forecasting performance of dynamic stochastic general equilibrium (DSGE) models, with and without financial frictions, for output growth and inflation in the period 1992 to 2011. We find strong evidence of time variation in the pool’s weights, reflecting the fact that the DSGE model with financial frictions produces superior forecasts in periods of financial distress but doesn’t perform as well in tranquil periods. The dynamic pool’s weights react in a timely fashion to changes in the environment, leading to real-time forecast improvements relative to other methods of density forecast combination, such as Bayesian model averaging, optimal (static) pools, and equal weights. We show how a policymaker dealing with model uncertainty could have used a dynamic pool to perform a counterfactual exercise (responding to the gap in labor market conditions) in the immediate aftermath of the Lehman crisis.
Tools
E-mail Alerts
By continuing to use our site, you agree to our Terms of Use and Privacy Statement. You can learn more about how we use cookies by reviewing our Privacy Statement. 
