Statistical Estimation and  Hypothesis Testing on Impulse Response Function

Iryna Rozora; Anastasiia Melnyk

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Statistical Estimation and Hypothesis Testing on Impulse Response Function

Authors

Iryna Rozora Taras Shevchenko NAtional University of Kyiv https://orcid.org/0000-0002-8733-7559
Anastasiia Melnyk Taras Shevchenko National University of Kyiv https://orcid.org/0000-0002-3167-4353

Abstract

In this paper a time-invariant continuous linear system is considered with a real-valued impulse response function (IRF) which is defined on a bounded domain. A sample input- output cross-correlogram is taken as an estimator of the response function. The input processes are supposed to be zero-mean stationary Gaussian process and can be repre- sented as a finite sum with uncorrelated terms. A rate of convergence of IRF estimator in the space L2([0,Λ]) is obtained that gives a possibility to propose a nonparametric goodness-of-fit testing on IRF.

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How to Cite

Rozora, I., & Melnyk, A. Statistical Estimation and Hypothesis Testing on Impulse Response Function. Austrian Journal of Statistics, 54(1), 200–213. Retrieved from https://ajs.or.at/index.php/ajs/article/view/1977

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Issue

Vol. 54 No. 1 (2025): Special Issue by the Department of Probability, Statistics and Actuarial Mathematics at TSNU of Kyiv

Section

Special Issue Department of Probability, Statistics and Actuarial Mathematics at TSNU of Kyiv

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