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Dating US business cycles with macro factors

Author

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  • Fossati Sebastian

    () (Department of Economics, University of Alberta, Edmonton, AB T6G 2H4, Canada)

Abstract

Latent factors estimated from panels of macroeconomic indicators are used to generate recession probabilities for the US economy. The focus is on current (rather than future) business conditions. Two macro factors are considered: (1) a dynamic factor estimated by maximum likelihood from a set of 4 monthly series; (2) the first of eight static factors estimated by principal components using a panel of 102 monthly series. Recession probabilities generated using standard probit, autoregressive probit, and Markov-switching models exhibit very different properties. Overall, a simple Markov-switching model based on the big data macro factor generates the sequence of out-of-sample class predictions that better approximates NBER recession months. Nevertheless, it is shown that the selection of the best performing model depends on the forecaster’s relative tolerance for false positives and false negatives.

Suggested Citation

  • Fossati Sebastian, 2016. "Dating US business cycles with macro factors," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(5), pages 529-547, December.
  • Handle: RePEc:bpj:sndecm:v:20:y:2016:i:5:p:529-547:n:4
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    References listed on IDEAS

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    1. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
    2. Dueker, Michael, 1999. "Conditional Heteroscedasticity in Qualitative Response Models of Time Series: A Gibbs-Sampling Approach to the Bank Prime Rate," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 466-472, October.
    3. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, January.
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    Citations

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    Cited by:

    1. Peter Egger & Michael Pfaffermayr, 2011. "Structural Estimation of Gravity Models with Path-Dependent Market Entry," FIW Research Reports series III-007, FIW.
    2. Fossati, Sebastian, 2017. "Testing for State-Dependent Predictive Ability," Working Papers 2017-9, University of Alberta, Department of Economics.

    More about this item

    Keywords

    business cycle; factors; forecasting; Markov-switching model; probit model;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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