Towards a general theory for nonlinear locally stationary processes
In this paper, some general theory is presented for locally stationary processes based on the stationary approximation and the stationary derivative. Laws of large numbers, central limit theorems as well as deterministic and stochastic bias expansions are proved for processes obeying an expansion in...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
6 March 2019
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| In: |
Bernoulli
Year: 2019, Volume: 25, Issue: 2, Pages: 1013-1044 |
| ISSN: | 1573-9759 |
| DOI: | 10.3150/17-BEJ1011 |
| Online Access: | Verlag, Volltext: https://doi.org/10.3150/17-BEJ1011 Verlag, Volltext: https://projecteuclid.org/euclid.bj/1551862842 
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| Author Notes: | Rainer Dahlhaus, Stefan Richter, Wei Biao Wu |
MARC
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| 520 | |a In this paper, some general theory is presented for locally stationary processes based on the stationary approximation and the stationary derivative. Laws of large numbers, central limit theorems as well as deterministic and stochastic bias expansions are proved for processes obeying an expansion in terms of the stationary approximation and derivative. In addition it is shown that this applies to some general nonlinear non-stationary Markov-models. In addition the results are applied to derive the asymptotic properties of maximum likelihood estimates of parameter curves in such models. | ||
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