Fornaro, Paolo (2015): Forecasting U.S. Recessions with a Large Set of Predictors.
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Abstract
In this paper, I use a large set of macroeconomic and financial predictors to forecast U.S. recession periods. I adopt Bayesian methodology with shrinkage in the parameters of the probit model for the binary time series tracking the state of the economy. The in-sample and out-of-sample results show that utilizing a large cross-section of indicators yields superior U.S. recession forecasts in comparison to a number of parsimonious benchmark models. Moreover, data rich models with shrinkage manage to beat the forecasts obtained with the factor-augmented probit model employed in past research
Item Type: | MPRA Paper |
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Original Title: | Forecasting U.S. Recessions with a Large Set of Predictors |
Language: | English |
Keywords: | Bayesian shrinkage, Business Cycles, Probit model, large cross-sections |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications |
Item ID: | 62973 |
Depositing User: | Dr. Paolo Fornaro |
Date Deposited: | 18 Mar 2015 12:13 |
Last Modified: | 27 Sep 2019 05:39 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/62973 |
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