On business cycle forecasting

Huiwen Lai () and Eric C. Y. Ng ()
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Huiwen Lai: Hong Kong Polytechnic University
Eric C. Y. Ng: Hong Kong University of Science and Technology

Frontiers of Business Research in China, 2020, vol. 14, issue 1, 1-26

Abstract: 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.

Keywords: Recession forecasting; Business cycle; Autoregressive Logit; Dynamic factor; Mixed data sampling (MIDAS) regression (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (17)

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DOI: 10.1186/s11782-020-00085-3

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