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Dynamic kernel models

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  • Pierluigi Vallarino

    (Erasmus University Rotterdam and Tinbergen Institute)

Abstract

This paper introduces the family of Dynamic Kernel models. These models study the predictive density function of a time series through a weighted average of kernel densities possessing a dynamic bandwidth. A general specification is presented and several particular models are studied in details. We propose an M-estimator for model parameters and derive its asymptotic properties under a misspecified setting. A consistent density estimator also introduced. Monte Carlo results show that the new models effectively track the time-varying distribution of several data generating processes. Dynamic Kernel models outperform extant kernel-based approaches in tracking the predictive distribution of GDP growth.

Suggested Citation

  • Pierluigi Vallarino, 2024. "Dynamic kernel models," Tinbergen Institute Discussion Papers 24-082/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20240082
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    File URL: https://papers.tinbergen.nl/24082.pdf
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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