The “probability of recession”: Evaluating probabilistic and non-probabilistic forecasts from probit models of U.S. recessions

Ratcliff, Ryan

doi:10.1016/j.econlet.2013.09.002

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Traditionally, variants of Probit/Logit models have been a major modeling device in the extensive and significant literature on forecasting recessions (e.g. Dueker, 1997;Estrella and Mishkin, 1998;Kauppi and Saikkonen, 2008;Nyberg, 2010;Ratcliff, 2013;among others). A disadvantage of the BRT approach relative to the kind of Probit/Logit models often used in that literature clearly is its relative computational complexity.…”

Section: Some Remarks On the Literaturementioning

confidence: 99%

“…16 The test results are not reported, but available from the authors upon request. 17 Ratcliff (2013) shows for a standard Probit/Logit model that a dynamic variant of such a model that includes the lagged recession indicator corresponds to a static model without a lagged recession indicator extended to include additional lags of the predictors according to a geometric weighting scheme.…”

Section: Notesmentioning

confidence: 99%

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Uncertainty and Forecasts of U.S. Recessions

Pierdzioch

Gupta

2019

Studies in Nonlinear Dynamics &Amp; Econometrics

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We estimate Boosted Regression Trees (BRT) on a sample of monthly data that extends back to 1889 to recover the predictive value of disaggregated news-based uncertainty indexes for U.S recessions. We control for widely-studied standard predictors and use out-of-sample metrics to assess forecast performance. We find that war-related uncertainty is among the top five predictors of recessions at three different forecast horizons (3, 6, and 12 months). The predictive value of war-related uncertainty has fallen in the second half of the 20th century. Uncertainty regarding the state of securities markets has gained in relative importance. The probability of a recession is a nonlinear function of war-related and securities-markets uncertainty. Receiver-operating-characteristic curves show that uncertainty improves out-of-sample forecast performance at the longer forecast horizons. A dynamic version of the BRT approach sheds light on the importance of various lags of government-related uncertainty for recession forecasting at the long forecast horizon.

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Section: Some Remarks On the Literaturementioning

confidence: 99%

Section: Notesmentioning

confidence: 99%

Uncertainty and Forecasts of U.S. Recessions

Pierdzioch

Gupta

2019

Studies in Nonlinear Dynamics &Amp; Econometrics

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“…Ratcliff () evaluates probabilistic and nonprobabilistic forecasts from probit models that use the slope of the yield curve to forecast recessions in the USA. The findings show that these models give reliable nonprobabilistic warnings of recessions, but the estimated probabilities do not match the conditional frequency of recession months.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting the Probability of Recessions in South Africa: the Role of Decomposed Term Spread and Economic Policy Uncertainty

Aye

Christou

Gil-Alana

et al. 2018

J of Intl Development

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This paper decomposes the term spread into the expectation and the term premium components using a fractional integration approach and subsequently uses same with the economic policy uncertainty index to forecast the probability of recession in South Africa. We use different specifications of the probit model and quarterly data from 1990:1 to 2012:1. Our out-of-sample results show that the model that incorporates the expectation component and economic policy uncertainty provides the best forecast of recession. All three recession periods in our sample were accurately dictated by the prediction models and the best forecast occurred at the four quarters ahead horizon. A robustness check with a longer sample from 1946q1 to 2017q4 but excluding the factors and economic policy uncertainty due to data limitation provided justification for decomposing the term spread as the model with the expected spread turned out to be the best. We draw the implications of these findings.

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“…This holds also for many other countries (see, e.g.,Ivanova et al, 2000 andBuchmann, 2011). Nevertheless,Ratcliff (2013) finds that while the term spread is useful in predicting whether there will be a recession or not, it does a poor job in capturing the probability of a recession.ECB Working Paper 1803, June 2015…”

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confidence: 99%