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Characterizing economic trends by Bayesian stochastic model specification search

Author

Listed:
  • Stefano Grassi

    (Aarhus University and CREATES)

  • Tommaso Proietti

    (Università di Roma “Tor Vergata”)

Abstract
We extend a recently proposed Bayesian model selection technique, known as stochastic model specification search, for characterising the nature of the trend in macroeconomic time series. In particular, we focus on autoregressive models with possibly time-varying intercept and slope and decide on whether their parameters are fixed or evolutive. Stochastic model specification is carried out to discriminate two alternative hypotheses concerning the generation of trends: the trend-stationary hypothesis, on the one hand, for which the trend is a deterministic function of time and the short run dynamics are represented by a stationary autoregressive process; the difference-stationary hypothesis, on the other, according to which the trend results from the cumulation of the effects of random disturbances. We illustrate the methodology for a set of U.S. macroeconomic time series, which includes the traditional Nelson and Plosser dataset. The broad conclusion is that most series are better represented by autoregressive models with time-invariant intercept and slope and coefficients that are close to boundary of the stationarity region. The posterior distribution of the autoregressive parameters, estimated by a suitable Gibbs sampling scheme, provides useful insight on quasi-integrated nature of the specifications selected.

Suggested Citation

  • Stefano Grassi & Tommaso Proietti, 2011. "Characterizing economic trends by Bayesian stochastic model specification search," CREATES Research Papers 2011-16, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2011-16
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    References listed on IDEAS

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    3. Filippo Ferroni & Stefano Grassi & Miguel A. Leon-Ledesma, 2015. "Fundamental shock selection in DSGE models," Studies in Economics 1508, School of Economics, University of Kent.

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    More about this item

    Keywords

    Bayesian model selection; stationarity; unit roots; stochastic trends; variable selection.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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