Abstract
AbstractLinear state space models (LSSMs) provide a very general framework for multiple time series analysis. We propose a novel statistical procedure for testing validity of a LSSM which is focused on the detection of changes in parameters of the given LSSM. We derive the moments as well as the asymptotic distribution of the test statistic, and investigate the test size and the test power for changes in means, variances, and autoregressive coefficients. In the empirical application we test the validity of LSSMs applied to daily realized volatility time series.
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