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A note on the monitoring of changes in linear models with dependent errors

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Dependence in Probability and Statistics

Part of the book series: Lecture Notes in Statistics ((LNS,volume 200))

Abstract

Horváth et al. [8] developed a monitoring procedure for detecting a change in the parameters of a linear regression model having independent and identically distributed errors. We extend these results to allow for dependent errors, which need not be independent of the stochastic regressors, and we also provide a class of consistent variance estimators. Our results cover strongly mixing errors as an important example. Applications to autoregressive time series and near-epoch dependent regressors are discussed, too.

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Author information

Authors and Affiliations

  1. Mathematisches Institut, Universität zu Köln, Weyertal 86-90, 50931, Köln, Germany

    Alexander Schmitz & Josef G. Steinebach

Authors
  1. Alexander Schmitz
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  2. Josef G. Steinebach
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Corresponding author

Correspondence to Alexander Schmitz .

Editor information

Editors and Affiliations

  1. UFR Sciences-Techniques, avenue Adolphe-Chauvin 2, Pontoise, 95302, France

    Paul Doukhan

  2. , UMR MIA 518, INRA AgroParisTech, Paris, 75005, France

    Gabriel Lang

  3. Stochastic Processes Department, Akademijos str. 4, Vilnius, 08412, Lithuania

    Donatas Surgailis

  4. Aarhus University, School of Economics, CREATES, Bartholins Allé 10, Aarhus C, 8000, Denmark

    Gilles Teyssière

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Schmitz, A., Steinebach, J.G. (2010). A note on the monitoring of changes in linear models with dependent errors. In: Doukhan, P., Lang, G., Surgailis, D., Teyssière, G. (eds) Dependence in Probability and Statistics. Lecture Notes in Statistics(), vol 200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14104-1_9

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