Arbeitspapier
Maximum likelihood estimation for non-stationary location models with mixture of normal distributions
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 flexible 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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. TI 2022-001/III
- Klassifikation
-
Wirtschaft
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
time-varying parameters
asymmetric and heavy-tailed distributions
robust filter
invertibility
consistency
asymptotic normality
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Blasques, Francisco
van Brummelen, Janneke
Gorgi, Paolo
Koopman, Siem Jan
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2022
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Blasques, Francisco
- van Brummelen, Janneke
- Gorgi, Paolo
- Koopman, Siem Jan
- Tinbergen Institute
Entstanden
- 2022
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