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Discussion of “An approach for identifying and predicting economic recessions in real‐time using time‐frequency functional models”by Holan, Yang, Matteson, and Wikle

Discussion of “An approach for identifying and predicting economic recessions in real‐time using... The business cycle is often modeled as the movement between two or more distinct economic regimes. Recent studies have reiterated the need for nonlinear dynamics in empirical economic models to fully capture differences in business cycle regimes. Consequently, both policymakers and private agents have incentives to forecast transitions from one regime to the other. Forecasting peaks—the period before the onset of recessions—may be particularly important. Unfortunately, previous studies have revealed a number of issues both with the setup and the performance of models used for forecasting recessions. Thus, before we can approach the issue of which model‐specification–data‐combination forecasts best, several key questions must be broached. One important issue is how the turning points are defined. Model‐based measures of turning points using latent variables are often subject to criticism of the underlying assumptions used to construct them. Although the The National Bureau of Economic Research (NBER)‐based dates may suffer some of the same criticisms, they do provide a fixed measure for which forecast evaluation can be made. Choosing to forecast a fixed measure like the NBER dates does not resolve all of the issues. Another choice in recession forecasting models—and the purpose of the current paper—is determining the set http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Discussion of “An approach for identifying and predicting economic recessions in real‐time using time‐frequency functional models”by Holan, Yang, Matteson, and Wikle

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References (5)

Publisher
Wiley
Copyright
Copyright © 2012 John Wiley & Sons, Ltd.
ISSN
1524-1904
eISSN
1526-4025
DOI
10.1002/asmb.1957
Publisher site
See Article on Publisher Site

Abstract

The business cycle is often modeled as the movement between two or more distinct economic regimes. Recent studies have reiterated the need for nonlinear dynamics in empirical economic models to fully capture differences in business cycle regimes. Consequently, both policymakers and private agents have incentives to forecast transitions from one regime to the other. Forecasting peaks—the period before the onset of recessions—may be particularly important. Unfortunately, previous studies have revealed a number of issues both with the setup and the performance of models used for forecasting recessions. Thus, before we can approach the issue of which model‐specification–data‐combination forecasts best, several key questions must be broached. One important issue is how the turning points are defined. Model‐based measures of turning points using latent variables are often subject to criticism of the underlying assumptions used to construct them. Although the The National Bureau of Economic Research (NBER)‐based dates may suffer some of the same criticisms, they do provide a fixed measure for which forecast evaluation can be made. Choosing to forecast a fixed measure like the NBER dates does not resolve all of the issues. Another choice in recession forecasting models—and the purpose of the current paper—is determining the set

Journal

Applied Stochastic Models in Business and IndustryWiley

Published: Nov 1, 2012

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