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Detecting serial dependencies with the reproducibility probability autodependogram

Author

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  • Luca Bagnato
  • Lucio De Capitani
  • Antonio Punzo

Abstract

The autodependogram is a graphical device recently proposed in the literature to analyze autodependencies. This paper proposes a normalization of this diagram taking into consideration the concept of reproducibility probability (RP). The result is a novel tool, named RP-autodependogram, which permits to study the strength and the stability of the evidence about the presence of lag-dependence. A simulation study on well-established time-series models is carried out to investigate the behavior of the RP-autodependogram also in comparison with other diagrams studying autodependencies. An application to financial data is finally considered to appreciate its usefulness in the identification of parametric/nonparametric models. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Luca Bagnato & Lucio De Capitani & Antonio Punzo, 2014. "Detecting serial dependencies with the reproducibility probability autodependogram," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(1), pages 35-61, January.
    • Handle: RePEc:spr:alstar:v:98:y:2014:i:1:p:35-61
    DOI: 10.1007/s10182-013-0208-y
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    References listed on IDEAS

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    1. Lijian Yang & Wolfgang Hardle & Jens Nielsen, 1999. "Nonparametric Autoregression with Multiplicative Volatility and Additive mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(5), pages 579-604, September.
    2. Zhou Zhou, 2012. "Measuring nonlinear dependence in time‐series, a distance correlation approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(3), pages 438-457, May.
    3. De Martini, Daniele, 2008. "Reproducibility probability estimation for testing statistical hypotheses," Statistics & Probability Letters, Elsevier, vol. 78(9), pages 1056-1061, July.
    4. Luca Bagnato & Antonio Punzo & Orietta Nicolis, 2012. "The autodependogram: a graphical device to investigate serial dependences," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(2), pages 233-254, March.
    5. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    6. De Capitani, L. & De Martini, D., 2011. "On stochastic orderings of the Wilcoxon Rank Sum test statistic--With applications to reproducibility probability estimation testing," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 937-946, August.
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    1. Bagnato, Luca & De Capitani, Lucio & Mazza, Angelo & Punzo, Antonio, 2015. "SDD: An R Package for Serial Dependence Diagrams," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(c02).
    2. Lucio De Capitani & Daniele De Martini, 2021. "Improving reproducibility probability estimation and preserving RP-testing," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 49-77, March.
    3. Mazza, Angelo & Punzo, Antonio, 2014. "DBKGrad: An R Package for Mortality Rates Graduation by Discrete Beta Kernel Techniques," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(c02).
    4. Luca Bagnato & Lucio De Capitani & Antonio Punzo, 2017. "A diagram to detect serial dependencies: an application to transport time series," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 581-594, March.
    5. Dimitris Christopoulos & Peter McAdam & Elias Tzavalis, 2023. "Exploring Okun's law asymmetry: An endogenous threshold logistic smooth transition regression approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(1), pages 123-158, February.
    6. L. Bagnato & L. De Capitani & A. Punzo, 2016. "The Kullback–Leibler autodependogram," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(14), pages 2574-2594, October.

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