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Robust Observation-Driven Models Using Proximal-Parameter Updates Abstract We propose an observation-driven modelling framework that permits time variation in the model’s parameters using a proximal-parameter (ProPar) update. ProPar maximizes the observation log-density with respect to the parameter vector, while penalizing the weighted ?2 norm relative to the one-step-ahead prediction. This yields an implicit stochastic-gradient update; taking instead the explicit version would produce the popular class of score-driven models. For log-concave observation densities (even when misspecified), ProPar’s robustness is evident from its muted response to outliers, stability under poorly specified learning rates, and global contractivity towards a pseudo-truth. We illustrate ProPar’s usefulness for estimating time-varying regressions, volatility, and quantiles.Classification-JEL: C10, C32, C51

Author

Listed:
  • Rutger-Jan Lange

    (Erasmus University Rotterdam)

  • Bram van Os

    (Erasmus University Rotterdam)

  • Dick van Dijk

    (Erasmus University Rotterdam)

Abstract

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Suggested Citation

  • Rutger-Jan Lange & Bram van Os & Dick van Dijk, 2022. "Robust Observation-Driven Models Using Proximal-Parameter Updates Abstract We propose an observation-driven modelling framework that permits time variation in the model’s parameters using a proximal-p," Tinbergen Institute Discussion Papers 22-066/III, Tinbergen Institute, revised 20 Dec 2022.
    • Handle: RePEc:tin:wpaper:20220066
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    References listed on IDEAS

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    Keywords

    Implicit gradient; Proximal point method; Robust filters; Score-driven methods; Time-varying parameter models.;
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