Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2022-01-06
Number: 22-001/III
Author-Name: Francisco Blasques
Author-Workplace-Name: Vrije Universiteit Amsterdam, Tinbergen Institute
Author-Name: Janneke van Brummelen
Author-Workplace-Name: Vrije Universiteit Amsterdam, Tinbergen Institute
Author-Name: Paolo Gorgi
Author-Workplace-Name: Vrije Universiteit Amsterdam, Tinbergen Institute
Author-Name: Siem Jan Koopman
Author-Workplace-Name: Vrije Universiteit Amsterdam, Tinbergen Institute, CREATES, Aarhus University
Title: Maximum Likelihood Estimation for Non-Stationary Location Models with Mixture of Normal Distributions
Abstract: We consider an observation-driven location model where the unobserved location variable is modeled as a random walk process and where the error variable is from a mixture of normal distributions. The mixed normal distribution can approximate many continuous error distributions accurately. We obtain a fexible modeling framework which is particularly designed for robust filtering and forecasting. We provide sufficient conditions for the strong consistency and asymptotic normality of the maximum likelihood estimator of the parameter vector in the specified model. The asymptotic properties are valid under correct model specification and can be
generalized to allow for potential misspecification of the model. A simulation study is carried out to monitor the forecast accuracy improvements when extra mixture components are added to the model. In an empirical study we show that our approach is able to outperform alternative observation-driven location models in forecast accuracy for a time-series of electricity spot prices.
Classification-JEL: C13, C22
Keywords: time-varying parameters, asymmetric and heavy-tailed distributions, robust filter, invertibility, consistency, asymptotic normality
File-URL: https://papers.tinbergen.nl/21001.pdf
File-Format: application/pdf
File-Size: 1.805.132 bytes
Handle: RePEc:tin:wpaper:20210001