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A General Framework for Observation Driven Time-Varying Parameter Models

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  • Drew Creal
  • Siem Jan Koopman
  • Andre Lucas

Abstract

We propose a new class of observation driven time series models that we refer to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled likelihood score. This provides a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The GAS model encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity, autoregressive conditional duration, autoregressive conditional intensity and single source of error models. In addition, the GAS specification gives rise to a wide range of new observation driven models. Examples include non-linear regression models with time-varying parameters, observation driven analogues of unobserved components time series models, multivariate point process models with time-varying parameters and pooling restrictions, new models for time-varying copula functions and models for time-varying higher order moments. We study the properties of GAS models and provide several non-trivial examples of their application.

Suggested Citation

  • Drew Creal & Siem Jan Koopman & Andre Lucas, 2009. "A General Framework for Observation Driven Time-Varying Parameter Models," Global COE Hi-Stat Discussion Paper Series gd08-038, Institute of Economic Research, Hitotsubashi University.
  • Handle: RePEc:hst:ghsdps:gd08-038
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    Cited by:

    1. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 7(2), pages 1-30, June.
    2. Michael Gibbs & Susanne Neckermann & Christoph Siemroth, 2017. "A Field Experiment in Motivating Employee Ideas," The Review of Economics and Statistics, MIT Press, pages 577-590.
    3. Hendrych, R. & Cipra, T., 2016. "On conditional covariance modelling: An approach using state space models," Computational Statistics & Data Analysis, Elsevier, pages 304-317.
    4. Neil Shephard, 2013. "Martingale unobserved component models," Economics Series Working Papers 644, University of Oxford, Department of Economics.
    5. Yvonne Adema & Jan Bonenkamp & Lex Meijdam, 2016. "Flexible pension take-up in social security," International Tax and Public Finance, Springer;International Institute of Public Finance, pages 316-342.
    6. Tsyplakov, Alexander, 2015. "Quasifiltering for time-series modeling," MPRA Paper 66453, University Library of Munich, Germany.
    7. repec:eee:insuma:v:75:y:2017:i:c:p:48-57 is not listed on IDEAS
    8. Andres, P. & Harvey, A., 2012. "The Dyanamic Location/Scale Model: with applications to intra-day financial data," Cambridge Working Papers in Economics 1240, Faculty of Economics, University of Cambridge.
    9. David E. Allen & Michael McAleer & Marcel Scharth, 2009. "Realized Volatility Risk," CARF F-Series CARF-F-197, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, revised Jan 2010.
    10. Xin Zhang & Drew Creal & Siem Jan Koopman & Andre Lucas, 2011. "Modeling Dynamic Volatilities and Correlations under Skewness and Fat Tails," Tinbergen Institute Discussion Papers 11-078/2/DSF22, Tinbergen Institute.
    11. Harvey, Andrew & Sucarrat, Genaro, 2014. "EGARCH models with fat tails, skewness and leverage," Computational Statistics & Data Analysis, Elsevier, pages 320-338.
    12. repec:bla:jtsera:v:38:y:2017:i:3:p:458-478 is not listed on IDEAS
    13. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2014. "Time Varying Transition Probabilities for Markov Regime Switching Models," Tinbergen Institute Discussion Papers 14-072/III, Tinbergen Institute.
    14. Harvey, Andrew & Sucarrat, Genaro, 2014. "EGARCH models with fat tails, skewness and leverage," Computational Statistics & Data Analysis, Elsevier, pages 320-338.

    More about this item

    Keywords

    dynamic models; time-varying parameters; non-linearity; exponential family; marked point processes; copulas;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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