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Maximum likelihood estimation for score-driven models

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  • Blasques, Francisco
  • van Brummelen, Janneke
  • Koopman, Siem Jan
  • Lucas, André

Abstract

We establish strong consistency and asymptotic normality of the maximum likelihood estimator for stochastic time-varying parameter models driven by the score of the predictive conditional likelihood function. For this purpose, we formulate primitive conditions for global identification, invertibility, strong consistency, and asymptotic normality both under correct specification and misspecification of the model. A detailed illustration is provided for a conditional volatility model with disturbances from the Student’s t distribution.

Suggested Citation

  • Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
    • Handle: RePEc:eee:econom:v:227:y:2022:i:2:p:325-346
    DOI: 10.1016/j.jeconom.2021.06.003
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    More about this item

    Keywords

    Time-varying parameters; Markov processes; Stationarity; Invertibility; Consistency; Asymptotic normality;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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