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Partial Sum Processes of Residual-Based and Wald-type Break-Point Statistics in Time Series Regression Models

Christis Katsouris

arXiv 31 Jan 2022 · Econometrics · 1 citations (OpenAlex)

arXiv:2202.00141 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We revisit classical asymptotics when testing for a structural break in linear regression models by obtaining the limit theory of residual-based and Wald-type processes. First, we establish the Brownian bridge limiting distribution of these test statistics. Second, we study the asymptotic behaviour of the partial-sum processes in nonstationary (linear) time series regression models. Although, the particular comparisons of these two different modelling environments is done from the perspective of the partial-sum processes, it emphasizes that the presence of nuisance parameters can change the asymptotic behaviour of the functionals under consideration. Simulation experiments verify size distortions when testing for a break in nonstationary time series regressions which indicates that the Brownian bridge limit cannot provide a suitable asymptotic approximation in this case. Further research is required to establish the cause of size distortions under the null hypothesis of parameter stability.

Citation extraction

54
references
76
in-text mentions
54
distinct cited
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11,949
main-text words

appendix boundary found by appendix_titled_section at “APPENDIX” · 83% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Kulperger, R., Yu, H., et al (2005) High moment partial sum processes of residuals in garch models and their applications0.81142100%
2Aue, A. and Horváth, L (2013) Structural breaks in time series0.7373367%
3Phillips, P. C. B. and Durlauf, S. N (1986) Multiple time series regression with integrated processes0.64422100%
4Bai, J (1997) Estimating multiple breaks one at a time0.64422100%
5Bai, J. and Perron, P (1998) Estimating and testing linear models with multiple structural changes0.64422100%
6Chu, C.-S. J., Stinchcombe, M., and White, H (1996) Monitoring structural change0.64422100%
7Katsouris, C (2021) Sequential break-point detection in stationary time series: An application to monitoring economic indicators self0.64422100%
8Pitarakis, J.-Y (2004) Least squares estimation and tests of breaks in mean and variance under misspecification0.64422100%
9Phillips, P. C. B (1987) Time series regression with a unit root0.58531100%
10Cavanagh, C. L., Elliott, G., and Stock, J. H (1995) Inference in models with nearly integrated regressors0.58531100%

Showing the top 10 of 54 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1BOOTSTRAPPING NONSTATIONARY AUTOREGRESSIVE PROCESSES WITH PREDICTIVE REGRESSION MODELS BY CHRISTIS KATSOURIS University of Southampton and University of Exeter0.64422
2Limit Theory under Network Dependence and Nonstationarity0.40511
3Break-Point Date Estimation for Nonstationary Autoregressive and Predictive Regression Models0.40511