Dynamic kernel models

Pierluigi Vallarino

No 24-082/III, Tinbergen Institute Discussion Papers from 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.

JEL-codes: C14 C51 C53 (search for similar items in EconPapers)
Date: 2024-12-31
New Economics Papers: this item is included in nep-ecm and nep-ets
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