Estimation pitfalls when the noise is not i.i.d.
Author:
Funder
JSPS Kiban grant
Publisher
Springer Science and Business Media LLC
Subject
General Medicine
Link
http://link.springer.com/article/10.1007/s42081-018-0004-8/fulltext.html
Reference18 articles.
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2. De Jong, P. (1987). A central limit theorem for generalized quadratic forms. Probab. Theory Relat. Fields, 75, 261–277.
3. Fox, R., & Taqqu, M. S. (1986). Large sample properties of parameter estimates for strongly dependent stationary Gaussian time series. Ann. Stat., 14, 517–532.
4. Fox, R., & Taqqu, M. S. (1987). Central limit theorems for quadratic forms in random variables having long-range dependence. Probab. Theory Relat. Fields, 74, 213–240.
5. Giraitis, L., & Surgailis, D. (1990). A central limit theorem for quadratic forms in strongly dependent linear variables and its application to asymptotic normality of Whittle’s estimate. Probab. Theory Relat. Fields, 86, 87–104.
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1. Parameter Estimation of Standard AR(1) and MA(1) Models Driven by a Non-I.I.D. Noise;Research Papers in Statistical Inference for Time Series and Related Models;2023
2. A note on limit theory for mildly stationary autoregression with a heavy-tailed GARCH error process;Statistics & Probability Letters;2019-09
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