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Predicting U.S. Recessions with Dynamic Binary Response Models

Heikki Kauppi and Pentti Saikkonen

Posted Online October 17, 2008
https://doi.org/10.1162/rest.90.4.777

Review of Economics and Statistics

Volume 90 | Issue 4 | November 2008
p.777-791
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Predicting U.S. Recessions with Dynamic Binary Response Models

Heikki Kauppi and Pentti Saikkonen
https://doi.org/10.1162/rest.90.4.777
Received: September 28, 2005
Accepted: July 20, 2007
Published Online: October 17, 2008
Abstract

Abstract

We develop dynamic binary probit models and apply them for predicting U.S. recessions using the interest rate spread as the driving predictor. The new models use lags of the binary response (a recession dummy) to forecast its future values and allow for the potential forecast power of lags of the underlying conditional probability. We show how multiperiod-ahead forecasts are computed iteratively using the same one-period-ahead model. Iterated forecasts that apply specific lags supported by statistical model selection procedures turn out to be more accurate than previously used direct forecasts based on horizon-specific model specifications.

Heikki Kauppi
Department of Economics, University of Turku
Pentti Saikkonen
Department of Mathematics and Statistics, University of Helsinki
https://doi.org/10.1162/rest.90.4.777
Abstract

Abstract

We develop dynamic binary probit models and apply them for predicting U.S. recessions using the interest rate spread as the driving predictor. The new models use lags of the binary response (a recession dummy) to forecast its future values and allow for the potential forecast power of lags of the underlying conditional probability. We show how multiperiod-ahead forecasts are computed iteratively using the same one-period-ahead model. Iterated forecasts that apply specific lags supported by statistical model selection procedures turn out to be more accurate than previously used direct forecasts based on horizon-specific model specifications.

Heikki Kauppi
Department of Economics, University of Turku
Pentti Saikkonen
Department of Mathematics and Statistics, University of Helsinki
Download Options

Predicting U.S. Recessions with Dynamic Binary Response Models

Heikki Kauppi and Pentti Saikkonen
https://doi.org/10.1162/rest.90.4.777
Received: September 28, 2005
Accepted: July 20, 2007
Published Online: October 17, 2008
Abstract

Abstract

We develop dynamic binary probit models and apply them for predicting U.S. recessions using the interest rate spread as the driving predictor. The new models use lags of the binary response (a recession dummy) to forecast its future values and allow for the potential forecast power of lags of the underlying conditional probability. We show how multiperiod-ahead forecasts are computed iteratively using the same one-period-ahead model. Iterated forecasts that apply specific lags supported by statistical model selection procedures turn out to be more accurate than previously used direct forecasts based on horizon-specific model specifications.

Heikki Kauppi
Department of Economics, University of Turku
Pentti Saikkonen
Department of Mathematics and Statistics, University of Helsinki
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