Predicting The Probability Of A Recession With Nonlinear Autoregressive Leading-Indicator Models
AbstractWe develop nonlinear leading-indicator models for GDP growth, with the interest-rate spread and growth in M2 as leading indicators. Since policy makers typically are interested in whether a recession is imminent, we evaluate these models according to their ability to predict the probability of a recession. Using data for the United States, we find that conditional on the spread, the marginal contribution of M2 growth in predicting recessions is negligible.
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Bibliographic InfoArticle provided by Cambridge University Press in its journal Macroeconomic Dynamics.
Volume (Year): 5 (2001)
Issue (Month): 04 (September)
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Other versions of this item:
- Anderson, H.M. & Vahid, F., 2000. "Predicting the Probability of a Recession with Nonlinear Autoregressive Leading Indicator Models," Monash Econometrics and Business Statistics Working Papers 3/00, Monash University, Department of Econometrics and Business Statistics.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
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