Semiparametric estimation by model selection for locally stationary processes
(2006) Journal of the Royal Statistical Society. Series B, statistical methodology — Vol. 68, n° 5, p. 721-746 (2006)
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- Abstract
- 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.
- Affiliations
Universität Heidelberg
Citations
Dahlhaus, R., & Van Bellegem, S. (2006). Semiparametric estimation by model selection for locally stationary processes. Journal of the Royal Statistical Society. Series B, statistical methodology, 68(5), 721-746. https://doi.org/10.1111/j.1467-9868.2006.00564.x (Original work published 2006)