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A Bayesian analysis of periodic integration
Author(s) -
Franses Philip Hans,
Koop Gary
Publication year - 1997
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(199712)16:7<509::aid-for671>3.0.co;2-8
Subject(s) - econometrics , bayesian probability , series (stratigraphy) , odds , economics , bayesian vector autoregression , statistics , sample (material) , mathematics , physics , geology , paleontology , logistic regression , thermodynamics
Recent empirical research into the seasonal and trend properties of macroeconomic time series using periodic models has resulted in strong evidence in favour of periodic integration (PI). PI implies that the differencing filter necessary to remove a stochastic trend varies across seasons and, hence, that seasonal fluctuations are related to the stochastic trend. Previous studies finding evidence of PI have used classical econometric techniques. In this paper, we investigate the possible sensitivity of this empirical result by using Bayesian techniques. An application of posterior odds analysis and highest posterior density interval tests to several quarterly UK macroeconomic series suggests strong evidence for PI, even when we allow for structural breaks in the deterministic seasonals. A predictive exercise indicates that PI usually outperforms other competing models in terms of out‐of‐sample forecasting. © 1997 John Wiley & Sons, Ltd.