A confidence interval test for the detection of structural breaks

Genç, İsmail H.; Arzaghi, Mohammad

doi:10.1016/j.jfranklin.2011.04.008

Cited by 7 publications

(6 citation statements)

References 52 publications

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“…Atypical observations, level shifts or variance change are common features of many real-life data sets [7,20,21]. Neglecting such effects may lead to inaccurate estimation of parameters of the model and in consequence inaccurate or a completely wrong prediction.…”

Section: Statistical Tests For Stationaritymentioning

confidence: 99%

“…In this case, the distribution is fitted to the residual series that is assumed to be i.i.d. But many independent variables seem to display changes in the underlying data generating process over time [7], therefore, they cannot be considered as an identically distributed sample. This typical behavior is also observed in time series described in Sec.…”

Section: Introductionmentioning

confidence: 99%

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Regime Variance Testing --- a Quantile Approach

Gajda¹,

Sikora²,

Wyłomańska³

2013

Acta Phys. Pol. B

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In this paper, we examine time series that exhibit behavior related to two or more regimes with different statistical properties. The motivation of our study are two real data sets from plasma physics with an observable two-regimes structure. In this paper, we develop a procedure to estimate the critical point of the division in a structural change in a time series. Moreover, we propose three tests to recognize such specific behavior. The presented methodology is based on the empirical second moment and its main advantage is the assumption of a lack of distribution. Moreover, the examined statistical properties are expressed in the language of empirical quantiles of the squared data, therefore, the methodology is an extension of the approach known from the literature. Theoretical results are confirmed by simulations and analysis of real data of turbulent laboratory plasma.

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Section: Statistical Tests For Stationaritymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Regime Variance Testing --- a Quantile Approach

Gajda¹,

Sikora²,

Wyłomańska³

2013

Acta Phys. Pol. B

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“…Mentioned above types of nonstationarity can be successfully tested and recognized from the data but they are not the only problems one may encounters during data analysis. Atypical observations, level shifts or variance change are common features of many real-life data sets [10,5,17]. Neglecting such effects may lead to inaccurate estimation of parameters of the model and in consequence inaccurate or completely wrong prediction.…”

Section: Statistical Tests For Stationaritymentioning

confidence: 99%

“…In this case the distribution is fitted to the residual series that is assumed to be i.i.d. But many independent variables seem to display changes in the underlying data generating process over time [10] therefore they can not be considered as identically distributed sample. This typical behavior we observe also in time series described in Section 2 that presents increments of floating potential fluctuations of turbulent laboratory plasma for the small torus radial position r = 37 cm.…”

Section: Introductionmentioning

confidence: 99%

Regime variance testing - a quantile approach

gajda,

Sikora,

Wyłomańska

2012

Preprint

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This paper is devoted to testing time series that exhibit behavior related to two or more regimes with different statistical properties. Motivation of our study are two real data sets from plasma physics with observable two-regimes structure. In this paper we develop estimation procedure for critical point of division the structure change of a time series. Moreover we propose three tests for recognition such specific behavior. The presented methodology is based on the empirical second moment and its main advantage is lack of the distribution assumption. Moreover, the examined statistical properties we express in the language of empirical quantiles of the squared data therefore the methodology is an extension of the approach known from the literature [5,17,1,13]. The theoretical results we confirm by simulations and analysis of real data of turbulent laboratory plasma.

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“…The debate as to how to proceed with all the techniques and their costs and benefits is a long one. The interested reader is referred toPerron and Vogelsang (1992), Papell (1997), Ben-David, Lumsdaine, andPapell (2003),Enders (2004),Shrestha and Chowdhury (2005),Glynn, Nelson and Reetu (2007) for a quick, but non-exhaustive review.5 We also conduct theGenc and Arzaghi (2011) test to specifically detect any structural break in the slope of the remittance data. We find no such break, which confirms our findings below.…”

mentioning

confidence: 99%

Structural Change in MENA Remittance Flows

Naufal

Genç

2013

SSRN Journal

Self Cite

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After independence, the GCC countries relied heavily on foreign workers from fellow Arabs countries. Thus, remittances flowed from GCC to other countries in MENA. In the 1980s-1990s labor source switched to South Asia, which we econometrically verify. This deprived several MENA labor exporters of large sums of foreign exchange, adding significant economic, social and political hardships on non-GCC MENA countries.

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A confidence interval test for the detection of structural breaks

Cited by 7 publications

References 52 publications

Regime Variance Testing --- a Quantile Approach

Regime Variance Testing --- a Quantile Approach

Regime variance testing - a quantile approach

Structural Change in MENA Remittance Flows

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