Beyond Location and Dispersion Models: The Generalized Structural Time Series Model with Applications

33 Pages Posted: 13 Mar 2015

See all articles by Abdelmajid Djennad

Abdelmajid Djennad

London Metropolitan University - Department of Computing, Communications Technology & Mathematics (CCTM); Government of the United Kingdom - Statistics, Modelling, and Economics Department

Robert Rigby

London Metropolitan University

Dimitrios Stasinopoulos

London Metropolitan University

Vlasios Voudouris

ABM Analytics Ltd

Paul H. C. Eilers

Erasmus University Rotterdam (EUR) - Erasmus Medical Center (MC)

Date Written: March 12, 2015

Abstract

In many settings of empirical interest, time variation in the distribution parameters is important for capturing the dynamic behaviour of time series processes. Although the fitting of heavy tail distributions has become easier due to computational advances, the joint and explicit modelling of time-varying conditional skewness and kurtosis is a challenging task. We propose a class of parameter-driven time series models referred to as the generalized structural time series (GEST) model. The GEST model extends Gaussian structural time series models by a) allowing the distribution of the dependent variable to come from any parametric distribution, including highly skewed and kurtotic distributions (and mixed distributions) and b) expanding the systematic part of parameter-driven time series models to allow the joint and explicit modelling of all the distribution parameters as structural terms and (smoothed) functions of independent variables. The paper makes an applied contribution in the development of a fast local estimation algorithm for the evaluation of a penalised likelihood function to update the distribution parameters over time without the need for evaluation of a high-dimensional integral based on simulation methods.

Keywords: non-Gaussian parameter-driven time series, fast local estimation algorithm, time-varying skewness, time-varying kurtosis.

JEL Classification: C1, C51, C52

Suggested Citation

Djennad, Abdelmajid and Rigby, Robert and Stasinopoulos, Dimitrios and Voudouris, Vlasios and Eilers, Paul H. C., Beyond Location and Dispersion Models: The Generalized Structural Time Series Model with Applications (March 12, 2015). Available at SSRN: https://ssrn.com/abstract=2577330 or http://dx.doi.org/10.2139/ssrn.2577330

Abdelmajid Djennad

London Metropolitan University - Department of Computing, Communications Technology & Mathematics (CCTM) ( email )

United Kingdom

Government of the United Kingdom - Statistics, Modelling, and Economics Department ( email )

United Kingdom

Robert Rigby

London Metropolitan University ( email )

166-220 Holloway Road
London EC3N 2EY, N7 8HN
United Kingdom

Dimitrios Stasinopoulos

London Metropolitan University ( email )

166-220 Holloway Road
London EC3N 2EY, N7 8HN
United Kingdom

Vlasios Voudouris (Contact Author)

ABM Analytics Ltd ( email )

Suite 17 125
145-157 St John Street
London, EC1V 4PW
United Kingdom

HOME PAGE: http://www.abm-analytics.com/people.php

Paul H. C. Eilers

Erasmus University Rotterdam (EUR) - Erasmus Medical Center (MC) ( email )

Doctor Molewaterplein 40
Rotterdam, South Holland 3015 GD
Netherlands

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
139
Abstract Views
687
rank
222,665
PlumX Metrics