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Maximum likelihood estimates for positive valued dynamic score models; The DySco package

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  • Andres, Philipp

Abstract

Recently, the Dynamic Conditional Score (DCS) or Generalized Autoregressive Score (GAS) time series models have attracted considerable attention. This motivates the need for a software package to estimate and evaluate these new models. A straightforward to operate program called the Dynamic Score (DySco) package is introduced for estimating models for positive variables, in which the location/scale evolves over time. Its capabilities are demonstrated using a financial application.

Suggested Citation

  • Andres, Philipp, 2014. "Maximum likelihood estimates for positive valued dynamic score models; The DySco package," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 34-42.
  • Handle: RePEc:eee:csdana:v:76:y:2014:i:c:p:34-42
    DOI: 10.1016/j.csda.2013.11.004
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    Cited by:

    1. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    2. David Ardia & Kris Boudt & Leopoldo Catania, 2016. "Generalized Autoregressive Score Models in R: The GAS Package," Papers 1609.02354, arXiv.org.
    3. Mauro Bernardi & Leopoldo Catania, 2016. "Comparison of Value-at-Risk models using the MCS approach," Computational Statistics, Springer, vol. 31(2), pages 579-608, June.

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