Generalized Linear Spectral Models for Locally Stationary Processes

Tommaso Proietti, Alessandra Luati () and Enzo D’Innocenzo ()
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Alessandra Luati: Imperial College London
Enzo D’Innocenzo: Vrije Universiteit Amsterdam

Chapter Chapter 13 in Research Papers in Statistical Inference for Time Series and Related Models, 2023, pp 343-368 from Springer

Abstract: Abstract A class of parametric models for locally stationary processes is introduced. The class depends on a power parameter that applies to the time-varying spectrum so that it can be locally represented by a (finite low dimensional) Fourier polynomial. The coefficients of the polynomial have an interpretation as time-varying autocovariances, whose dynamics are determined by a linear combination of smooth transition functions, depending on some static parameters. Frequency domain estimation is based on the generalized Whittle likelihood and the pre-periodogram, while model selection is performed through information criteria. Change points are identified via a sequence of score tests. Consistency and asymptotic normality are proved for the parametric estimators considered in the paper, under weak assumptions on the time-varying parameters.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-99-0803-5_13

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DOI: 10.1007/978-981-99-0803-5_13

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