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

Abdelhakim Aknouche and Stefanos Dimitrakopoulos

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Proportional time series data; Beta-ARMA model; Simplex ARMA; Autoregressive conditional duration; Exponential QMLE. (search for similar items in EconPapers)
JEL-codes: C13 C22 C25 C46 C51 C58 (search for similar items in EconPapers)
Date: 2021-12-06, Revised 2021-12-06
New Economics Papers: this item is included in nep-ecm and nep-ets
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