Structural change in panel data is a widespread phenomena. This paper proposes a fluctuation test to detect a structural change at an unknown date in heterogeneous panel data models with or without common correlated e...Structural change in panel data is a widespread phenomena. This paper proposes a fluctuation test to detect a structural change at an unknown date in heterogeneous panel data models with or without common correlated effects. The asymptotic properties of the fluctuation statistics in two cases are developed under the null and local alternative hypothesis. Furthermore, the consistency of the change point estimator is proven. Monte Carlo simulation shows that the fluctuation test can control the probability of type I error in most cases, and the empirical power is high in case of small and moderate sample sizes. An application of the procedure to a real data is presented.展开更多
Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.Traditional causality inference methods have a salient limitation that the model must be linear...Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.Traditional causality inference methods have a salient limitation that the model must be linear and with Gaussian noise.Although additive model regression can effectively infer the nonlinear causal relationships of additive nonlinear time series,it suffers from the limitation that contemporaneous causal relationships of variables must be linear and not always valid to test conditional independence relations.This paper provides a nonparametric method that employs both mutual information and conditional mutual information to identify causal structure of a class of nonlinear time series models,which extends the additive nonlinear times series to nonlinear structural vector autoregressive models.An algorithm is developed to learn the contemporaneous and the lagged causal relationships of variables.Simulations demonstrate the effectiveness of the proposed method.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos. 11801438,12161072 and 12171388the Natural Science Basic Research Plan in Shaanxi Province of China under Grant No. 2023-JC-YB-058the Innovation Capability Support Program of Shaanxi under Grant No. 2020PT-023。
文摘Structural change in panel data is a widespread phenomena. This paper proposes a fluctuation test to detect a structural change at an unknown date in heterogeneous panel data models with or without common correlated effects. The asymptotic properties of the fluctuation statistics in two cases are developed under the null and local alternative hypothesis. Furthermore, the consistency of the change point estimator is proven. Monte Carlo simulation shows that the fluctuation test can control the probability of type I error in most cases, and the empirical power is high in case of small and moderate sample sizes. An application of the procedure to a real data is presented.
基金supported by the National Natural Science Foundation of China under Grant Nos.60972150 and 10926197
文摘Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.Traditional causality inference methods have a salient limitation that the model must be linear and with Gaussian noise.Although additive model regression can effectively infer the nonlinear causal relationships of additive nonlinear time series,it suffers from the limitation that contemporaneous causal relationships of variables must be linear and not always valid to test conditional independence relations.This paper provides a nonparametric method that employs both mutual information and conditional mutual information to identify causal structure of a class of nonlinear time series models,which extends the additive nonlinear times series to nonlinear structural vector autoregressive models.An algorithm is developed to learn the contemporaneous and the lagged causal relationships of variables.Simulations demonstrate the effectiveness of the proposed method.
