Semiparametric estimation by model selection for locally stationary processes

Sebastien Van Bellegem () and Rainer Dahlhaus ()

Journal of the Royal Statistical Society Series B, 2006, vol. 68, issue 5, 721-746

Abstract: Summary. Over recent decades increasingly more attention has been paid to the problem of how to fit a parametric model of time series with time‐varying parameters. A typical example is given by autoregressive models with time‐varying parameters. We propose a procedure to fit such time‐varying models to general non‐stationary processes. The estimator is a maximum Whittle likelihood estimator on sieves. The results do not assume that the observed process belongs to a specific class of time‐varying parametric models. We discuss in more detail the fitting of time‐varying AR(p) processes for which we treat the problem of the selection of the order p, and we propose an iterative algorithm for the computation of the estimator. A comparison with model selection by Akaike's information criterion is provided through simulations.

Date: 2006
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Citations: View citations in EconPapers (9)

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https://doi.org/10.1111/j.1467-9868.2006.00564.x

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