Testing Coefficient Randomness in Multivariate Random Coefficient Autoregressive Models Based on Locally Most Powerful Test

Li Bi (), Deqi Wang, Libo Cheng and Dequan Qi
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Li Bi: School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
Deqi Wang: School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
Libo Cheng: School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
Dequan Qi: School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China

Mathematics, 2024, vol. 12, issue 16, 1-21

Abstract: The multivariate random coefficient autoregression (RCAR) process is widely used in time series modeling applications. Random autoregressive coefficients are usually assumed to be independent and identically distributed sequences of random variables. This paper investigates the issue of coefficient constancy testing in a class of static multivariate first-order random coefficient autoregressive models. We construct a new test statistic based on the locally most powerful-type test and derive its limiting distribution under the null hypothesis. The simulation compares the empirical sizes and powers of the LMP test and the empirical likelihood test, demonstrating that the LMP test outperforms the EL test in accuracy by 10.2%, 10.1%, and 30.9% under conditions of normal, Beta-distributed, and contaminated errors, respectively. We provide two sets of real data to illustrate the practical effectiveness of the LMP test.

Keywords: hypothesis test; locally most powerful test; multivariate RCAR(1) model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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