Predictive Power of Markovian Models: Evidence from U.S. Recession Forecasting

36 Pages Posted: 9 Apr 2018

See all articles by Ruilin Tian

Ruilin Tian

North Dakota State University - Department of Accounting, Finance, and Information Systems

Gang Shen

North Dakota State University - Department of Statistics

Date Written: March 14, 2018

Abstract

This paper brings new evidence of predicting the U.S. recessions through Markovian models. The Markovian models, including the Hidden Markov and Markov models, incorporate the temporal autocorrelation of binary recession indicators in a more traditional and natural way. Considering interest rates and spreads, stock prices, monetary aggregates, and output as the candidate predictors, we examine the out-of-sample performance of the Markovian models in predicting the recessions one to twelve months ahead. The Markovian models are superior to the probit models in detecting a recession and capturing the recession duration. We find the "one-month lag phenomenon" that the best Markovian model supported by statistical model selection procedures can always recognize the onset of a recession one month after it starts. In addition, the yield spread continues to serve as the most ecient predictor variable in explaining business cycles.

Keywords: Forecast Recession, Hidden Markov Model, Markov Model, Probit Model, Recession Indicator, GDI.

JEL Classification: C53, E32, E37, E47.

Suggested Citation

Tian, Ruilin and Shen, Gang, Predictive Power of Markovian Models: Evidence from U.S. Recession Forecasting (March 14, 2018). Available at SSRN: https://ssrn.com/abstract=3152699 or http://dx.doi.org/10.2139/ssrn.3152699

Ruilin Tian (Contact Author)

North Dakota State University - Department of Accounting, Finance, and Information Systems ( email )

Fargo, ND
United States
7012316544 (Phone)
7012316545 (Fax)

Gang Shen

North Dakota State University - Department of Statistics ( email )

NDSU Dept 2770
PO Box 6050
Fargo, ND 58108-6050
United States

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