Structural Break Detection in Autoregressional Conditional Heteroskedasticity Model: Case of Student’s Distribution

Structural Break Detection in Autoregressional Conditional Heteroskedasticity Model: Case of Student’s Distribution

Two methods of structural break detection in a piecewise generalized model of autoregressive conditional heteroscedasticity are considered. The first method is based on Kolmogorov–Smirnov statistics and is called the KS method. The second one is based on the cumulative sums and is called the KL meth...

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Veröffentlicht in:Mathematical models and computer simulations 2023-08, Vol.15 (4), p.654-659
Hauptverfasser: Borzykh, D. A., Yazykov, A. A.
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Autoregressive models
Mathematical Modeling and Industrial Mathematics
Mathematics
Mathematics and Statistics
Random errors
Simulation and Modeling
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description Two methods of structural break detection in a piecewise generalized model of autoregressive conditional heteroscedasticity are considered. The first method is based on Kolmogorov–Smirnov statistics and is called the KS method. The second one is based on the cumulative sums and is called the KL method. In this paper, the KS and KL methods are compared under the assumption of Student’s conditional distribution of random errors. The results of our Monte Carlo experiments are as follows: the KL method is inferior to the KS method both in terms of the average probability of errors of the first type and in terms of the average power of detecting a structural break.
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subjects Autoregressive models
Mathematical Modeling and Industrial Mathematics
Mathematics
Mathematics and Statistics
Random errors
Simulation and Modeling
title Structural Break Detection in Autoregressional Conditional Heteroskedasticity Model: Case of Student’s Distribution
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