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Checking Serial Independence of Residuals from a Nonlinear Model

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Challenges at the Interface of Data Analysis, Computer Science, and Optimization

Abstract

In this paper the serial independence tests known as SIS (Serial Independence Simultaneous) and SICS (Serial Independence Chi-Square) are considered. These tests are here contextualized in the model validation phase for nonlinear models in which the underlying assumption of serial independence is tested on the estimated residuals. Simulations are used to explore the performance of the tests, in terms of size and power, once a linear/nonlinear model is fitted on the raw data. Results underline that both tests are powerful against various types of alternatives.

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References

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Author information

Authors and Affiliations

  1. Dipartimento di Metodi Quantitativi per le Scienze Economiche e Aziendali, Università di Milano-Bicocca, Milano, Italy

    Luca Bagnato

  2. Dipartimento di Impresa, Culture e Società, Università di Catania, Catania, Italy

    Antonio Punzo

Authors
  1. Luca Bagnato
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  2. Antonio Punzo
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Corresponding author

Correspondence to Antonio Punzo .

Editor information

Editors and Affiliations

  1. Fak. Wirtschaftswissenschaften, Inst. Entscheidungstheorieund, Universität Karlsruhe (TH), Kaiserstr. 12, Karlsruhe, 76128, Germany

    Wolfgang A. Gaul

  2. Insitute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Baden-Württemberg, Germany

    Andreas Geyer-Schulz

  3. , Information Systems, University ofHildesheim, Marienburger Platz 22, Hildesheim, 31141, Germany

    Lars Schmidt-Thieme

  4. Institute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, Karlsruhe, 76128, Germany

    Jonas Kunze

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Bagnato, L., Punzo, A. (2012). Checking Serial Independence of Residuals from a Nonlinear Model. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24466-7_21

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