Abstract
We develop a recession forecasting framework using a less restrictive target variable and more flexible and inclusive specification than those used in the literature. The target variable captures the occurrence of a recession within a given future period rather than at a specific future point in time (widely used in the literature). The modeling specification combines an autoregressive Logit model capturing the autocorrelation of business cycles, a dynamic factor model encompassing many economic and financial variables, and a mixed data sampling regression incorporating common factors with mixed sampling frequencies. The model generates significantly more accurate forecasts for U.S. recessions with smaller forecast errors and stronger early signals for the turning points of business cycles than those generated by existing models.
Highlights
The importance of accurately detecting turning points in the business cycle cannot be overstated
We show that our unified approach can improve forecasting performance by exploring three channels simultaneously: channel (a)—one using a more flexible Logit or Probit model specification to capture the potential autocorrelation structure of business cycles; channel (b)—one incorporating the information embedded in a large number of low-frequency economic and financial variables; and channel (c)—one encompassing many relatively highfrequency financial and economic variables, whose information content has seldom been explored in recession forecasting because of data aggregation issues
We propose a unified framework that simultaneously addresses the three modeling channels described in Key methodologies in recession forecasting section: (1) It uses a flexible functional form by including the lagged recession probability function in an autoregressive Logit model of recession forecasting,10 (2) it employs dynamic factor modeling (DFM) to extract common factors from many monthly or weekly economic and financial variables, and (3) it applies the MIDAS principle to incorporate the mixed-frequency common factors in the autoregressive Logit framework
Summary
The importance of accurately detecting turning points in the business cycle cannot be overstated. An accurate forecast of economic outlook is important for practitioners such as bankers and fund managers to make timely investment decisions and for policymakers such as central bankers to implement preemptive policies. Investigating the 2001 recession in the United States, for example, Stock and Watson (2003) examine the Survey of Professional Forecasters published in the fourth quarter of 2000. They find that forecasters saw only an 11% chance of negative GDP growth in the first quarter of 2001, consistent with their optimistic growth forecast of
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
