Logo
Munich Personal RePEc Archive

Autoregressive conditional proportion: A multiplicative-error model for (0,1)-valued time series

Aknouche, Abdelhakim and Dimitrakopoulos, Stefanos (2021): Autoregressive conditional proportion: A multiplicative-error model for (0,1)-valued time series.

[thumbnail of MPRA_paper_110954.pdf]
Preview
PDF
MPRA_paper_110954.pdf

Download (664kB) | Preview

Abstract

We propose a multiplicative autoregressive conditional proportion (ARCP) model for (0,1)-valued time series, in the spirit of GARCH (generalized autoregressive conditional heteroscedastic) and ACD (autoregressive conditional duration) models. In particular, our underlying process is defined as the product of a (0,1)-valued iid sequence and the inverted conditional mean, which, in turn, depends on past reciprocal observations in such a way that is larger than unity. The probability structure of the model is studied in the context of the stochastic recurrence equation theory, while estimation of the model parameters is performed by the exponential quasi-maximum likelihood estimator (EQMLE). The consistency and asymptotic normality of the EQMLE are both established under general regularity assumptions. Finally, the usefulness of our proposed model is illustrated with simulated and two real datasets.

Atom RSS 1.0 RSS 2.0

Contact us: mpra@ub.uni-muenchen.de

This repository has been built using EPrints software.

MPRA is a RePEc service hosted by Logo of the University Library LMU Munich.