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Spurious periodic autoregressions

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  • TOMMASO PROIETTI

Abstract

The class of periodic autoregressive (PAR) models, suitably extended so as to allow for 'periodic integration', has recently found widespread application to economic time series as an alternative to the time-invariant models available in the literature. An elaborate modelling strategy has been proposed, and new tests for periodic integration have been envisaged, whose empirical performance tends to support the notion that the kind of non-stationary stochastic dynamics observed in time series arises as a consequence of periodic integration. This paper aims at challenging this view by means of a Monte Carlo experiment: we generate data according to a trend with a seasonality model such that the trend is a random walk with drift and the seasonal component is generated according to a stochastic trigonometric model. It is found that all the fundamental tools of PAR modelling will tend to provide spurious evidence in favour of a periodic model, and conclude that, as long as macroeconomic time series are concerned, PAR models are an overelaborate way of capturing essential features, such as indeterministic trends and seasonals, that are more parsimoniously accommodated by a time-invariant model.

Suggested Citation

  • Tommaso Proietti, 1998. "Spurious periodic autoregressions," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages 1-22.
    • Handle: RePEc:ect:emjrnl:v:1:y:1998:i:conferenceissue:p:c1-c22
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    Cited by:

    1. Pami Dua & Lokendra Kumawat, 2005. "Modelling and Forecasting Seasonality in Indian Macroeconomic Time Series," Working papers 136, Centre for Development Economics, Delhi School of Economics.
    2. Clements, Michael & Smith, Jeremy, 1997. "Forecasting Seasonal Uk Consumption Components," Economic Research Papers 268761, University of Warwick - Department of Economics.

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