Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search
AbstractAn important issue in modelling economic time series is whether key unobserved components representing trends, seasonality and calendar components, are deterministic or evolutive. We address it by applying a recently proposed Bayesian variable selection methodology to an encompassing linear mixed model that features, along with deterministic effects, additional random explanatory variables that account for the evolution of the underlying level, slope, seasonality and trading days. Variable selection is performed by estimating the posterior model probabilities using a suitable Gibbs sampling scheme. The paper conducts an extensive empirical application on a large and representative set of monthly time series concerning industrial production and retail turnover. We find strong support for the presence of stochastic trends in the series, either in the form of a time-varying level, or, less frequently, of a stochastic slope, or both. Seasonality is a more stable component: only in 70% of the cases we were able to select at least one stochastic trigonometric cycle out of the six possible cycles. Most frequently the time variation is found in correspondence with the fundamental and the first harmonic cycles. An interesting and intuitively plausible finding is that the probability of estimating time-varying components increases with the sample size available. However, even for very large sample sizes we were unable to find stochastically varying calendar effects.
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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-30.
Date of creation: 02 Sep 2011
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Web page: http://www.econ.au.dk/afn/
Bayesian model selection; stationarity; unit roots; stochastic trends; variable selection.;
Other versions of this item:
- Grassi, Stefano & Proietti, Tommaso, 2011. "Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search," Working Papers 1 OMEWP, University of Sydney Business School, Discipline of Business Analytics.
- 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
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-09-16 (All new papers)
- NEP-ECM-2011-09-16 (Econometrics)
- NEP-ETS-2011-09-16 (Econometric Time Series)
- NEP-MAC-2011-09-16 (Macroeconomics)
- NEP-ORE-2011-09-16 (Operations Research)
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