The Cusum of Squares Test for Scale Changes in Infinite Order Moving Average Processes

Lee, Sangyeol; Park, Siyun

doi:10.1111/1467-9469.00259

Cited by 94 publications

(51 citation statements)

References 24 publications

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“…In time series models, this problem has been investigated by many authors. See, for example, Tsay (1988) and Lee and Park (2001). However, to the best of our knowledge, few works are devoted to integer-valued time series models.…”

Section: Introductionmentioning

confidence: 99%

Robust CUSUM test for time series of counts and its application to analyzing the polio incidence data

Kang¹

2015

Journal of the Korean Data and Information Science Society

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In this paper, we analyze the polio incidence data based on the Poisson autoregressive models, focusing particularly on change-point detection. Since the data include some strongly deviating observations, we employ the robust cumulative sum (CUSUM) test proposed by Kang and Song (2015) to perform the test for parameter change. Contrary to the result of Kang and Lee (2014), our data analysis indicates that there is no significant change in the case of the CUSUM test with strong robustness and the same result is obtained after ridding the polio data of outliers. We additionally consider the comparison of the forecasting performance. All the results demonstrate that the robust CUSUM test performs adequately in the presence of seemingly outliers.Keywords: Minimum density power divergence estimator, Poisson autoregressive model, robust parameter change test, the polio incidence data.

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Section: Introductionmentioning

confidence: 99%

Robust CUSUM test for time series of counts and its application to analyzing the polio incidence data

Kang¹

2015

Journal of the Korean Data and Information Science Society

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“…One procedure that has played a particularly important role in the study of structural change is the CUSUM test proposed by Brown, Durbin, Evans (1975) and extended in a variety of ways by Krämer, Ploberger, and Alt (1988), Ploberger and Krämer (1992), Kuan and Hornik (1995), and Lee and Park (2001), to name just a few. Compared to some other tests in the literature (e.g., Andrews, 1993;Andrews and Ploberger, 1994), CUSUM-type tests are computationally simple and thus easier to implement in practice.…”

Section: Introductionmentioning

confidence: 99%

Testing Structural Change in Partially Linear Models

Su¹,

White²

2010

Econom. Theory

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We consider two tests of structural change for partially linear time-series models. The first tests for structural change in the parametric component, based on the cumulative sums of gradients from a single semiparametric regression. The second tests for structural change in the parametric and nonparametric components simultaneously, based on the cumulative sums of weighted residuals from the same semiparametric regression. We derive the limiting distributions of both tests under the null hypothesis of no structural change and for sequences of local alternatives. We show that the tests are generally not asymptotically pivotal under the null but may be free of nuisance parameters asymptotically under further asymptotic stationarity conditions. Our tests thus complement the conventional instability tests for parametric models. To improve the finite sample performance of our tests, we also propose a wild bootstrap version of our tests and justify its validity. Finally, we conduct a small set of Monte Carlo simulations to investigate the finite sample properties of the tests.JEL classifications: C12, C14, C22, C5 * The authors gratefully thank Oliver Linton, and two anonymous referees for their many constructive comments on the previous versions of the paper. They also thank

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“…Today, time series with structural breaks are important models in econometrics as economic and financial time series are frequently affected by monetary policy and critical social events that may cause structural change. See, inter alia, Wichern et al (1976), Perron (1989), Ploberger and Krämer (1992), Andrews (1993), Hidalgo (1995), Lavielle (1999), and Lee and Park (2001).…”

Section: Introductionmentioning

confidence: 99%

Testing structural change in time-series nonparametric regression models

Zhang

2008

Statistics and Its Interface

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We propose a CUSUM type of test for structural change in dynamic nonparametric regression models. It is based upon the cumulative sums of weighted residuals from a single nonparametric regression and complements the conventional parameter instability tests in parametric models. We derive the limiting distributions of the test under both the null hypothesis and sequences of local alternatives. A bootstrap procedure is also proposed and its validity is justified. Finally, simulation experiments are conducted to investigate the finite sample properties of our test.

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The Cusum of Squares Test for Scale Changes in Infinite Order Moving Average Processes

Cited by 94 publications

References 24 publications

Robust CUSUM test for time series of counts and its application to analyzing the polio incidence data

Robust CUSUM test for time series of counts and its application to analyzing the polio incidence data

Testing Structural Change in Partially Linear Models

Testing structural change in time-series nonparametric regression models

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