Forecasting transaction counts with integer-valued GARCH models

被引：8

作者：

Aknouche, Abdelhakim ^{[1
,2
]}

Almohaimeed, Bader S. ^{[1
]}

Dimitrakopoulos, Stefanos ^{[3
]}

机构：

[1] Qassim Univ, Coll Sci, Dept Math, POB 707, Buraydah 51431, Saudi Arabia

[2] Univ Sci & Technol Houari Boumediene, Fac Math, Bab Ezzouar, Algeria

[3] Univ Leeds, Leeds Univ Business Sch, Econ Div, Leeds LS2 9JT, W Yorkshire, England

来源：

STUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS | 2022年 / 26卷 / 04期

关键词：

count time series; forecasting; INGARCH models; MCMC; QUASI-LIKELIHOOD INFERENCE; TIME-SERIES; POISSON;

D O I：

10.1515/snde-2020-0095

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Using numerous transaction data on the number of stock trades, we conduct a forecasting exercise with INGARCH models, governed by various conditional distributions; the Poisson, the linear and quadratic negative binomial, the double Poisson and the generalized Poisson. The model parameters are estimated with efficient Markov Chain Monte Carlo methods, while forecast evaluation is done by calculating point and density forecasts.

引用

页码：529 / 539

页数：11

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