On business cycle forecasting

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number17
Journal / PublicationFrontiers of Business Research in China
Volume14
Online published7 Sept 2020
Publication statusPublished - 2020
Externally publishedYes

Link(s)

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.

Research Area(s)

  • Autoregressive Logit, Business cycle, Dynamic factor, Mixed data sampling (MIDAS) regression, Recession forecasting

Citation Format(s)

On business cycle forecasting. / Lai, Huiwen; Ng, Eric C. Y.
In: Frontiers of Business Research in China, Vol. 14, 17, 2020.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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