Structural Change Monitoring for Random Coefficient Autoregressive Time Series

Li, Fu-Xiao; Tian, Zheng; Qi, Peiyan

doi:10.1080/03610918.2013.800205

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…In order of monitoring the financial trends modeled using the time series framework of ARMA-GARCH, A method was suggested by Doroudyan et al [5] on the basis of the Shewhart control chart. Tian et al [9] suggested a monitoring plan to identify a basic change in the randomized coefficient autoregressive model of time series of the order p (RCA(p)) in sequence, following the training duration of size T.…”

Section: Introductionmentioning

confidence: 99%

Change Point Estimation of the Stationary State in Auto Regressive Moving Average Models, Using Maximum Likelihood Estimation and Singular Value Decomposition-based Filtering

Sheikhrabori

Aminnayeri

Ayoubi

2019

IJE

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In this paper, for the first time, the subject of change point estimation has been utilized in the stationary state of auto regressive moving average (ARMA) (1, 1). In the monitoring phase, in case the features of the question pursue a time series, i.e., ARMA(1,1), on the basis of the maximum likelihood technique, an approach will be developed for the estimation of the stationary state's change point. To estimate unidentified parameters following the change point, the dynamic linear model's filtering was utilized on the basis of the singular decomposition of values. The proposed model has wide applications in several fields such as finance, stock exchange marks and rapid production. The results of simulation showed the suggested estimator's effectiveness. In addition, a real example on stock exchange market is offered to delineate the application.

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Section: Introductionmentioning

confidence: 99%

Change Point Estimation of the Stationary State in Auto Regressive Moving Average Models, Using Maximum Likelihood Estimation and Singular Value Decomposition-based Filtering

Sheikhrabori

Aminnayeri

Ayoubi

2019

IJE

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“…Carsoule and Frances [4] considered a procedure based on maximum likelihood scores under normality assumptions and sketched some theoretical results. Quasi-maximum likelihood based monitoring in random coefficient autoregression was considered in Li et al [14], Na et al [17] and Prášková [18].…”

Section: Introductionmentioning

confidence: 99%

Change point detection in vector autoregression

Prášková¹

2018

Kybernetika

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On the Detection of Structural Breaks: The Case of the Covid Shock

Hall,

Tavlas,

Trapani

et al. 2024

Journal of Forecasting

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Both the Federal Reserve (Fed) and the European Central Bank (ECB) have been criticized for not having perceived that the outbreak of Covid at the beginning of 2020 would lead to a structural change in inflation in the early 2020s. Both central banks viewed the initial inflation surge in 2021 as temporary and delayed monetary tightening until 2022. We argue that the existing literature on structural breaks could not have been useful to policymakers because it identifies the breaks in an arbitrary way. The tests used to identify breaks do not incorporate prior knowledge that a break may have occurred so that the tests have very little power to detect a break that occurs at the end of the sample. We show that, in the event of a major shock, such as Covid, using knowledge that a break may have occurred and testing for a break in a recursive way as new data become available could have alerted policymakers to the break in inflation. We conduct Monte Carlo simulations suggesting that our method would have identified that a break had occurred in inflation by the end of 2020, well before policymakers had perceived the break.

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Structural Change Monitoring for Random Coefficient Autoregressive Time Series

Cited by 9 publications

References 17 publications

Change Point Estimation of the Stationary State in Auto Regressive Moving Average Models, Using Maximum Likelihood Estimation and Singular Value Decomposition-based Filtering

Change Point Estimation of the Stationary State in Auto Regressive Moving Average Models, Using Maximum Likelihood Estimation and Singular Value Decomposition-based Filtering

Change point detection in vector autoregression

On the Detection of Structural Breaks: The Case of the Covid Shock

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