Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/311620 
Year of Publication: 
2024
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2024-082/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
This paper introduces the family of Dynamic Kernel models. These models approximate 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 detail. We propose an Mestimator for model parameters and derive its asymptotic properties under a misspecified setting. A consistent density estimator is 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.
Subjects: 
Time-varying density function
time-varying parameter models
M estimation
density forecasts
Document Type: 
Working Paper

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