FUNCTIONAL-COEFFICIENT AUTOREGRESSIVE MODELS

被引：356

作者：

CHEN, R ^{[1
]}

TSAY, RS ^{[1
]}

机构：

[1] UNIV CHICAGO, GRAD SCH BUSINESS, CHICAGO, IL 60637 USA

来源：

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 1993年 / 88卷 / 421期

关键词：

ARRANGED LOCAL REGRESSION; CONSISTENCY; FORECASTING; GEOMETRICAL ERGODICITY; NONLINEAR TIME SERIES; THRESHOLD AUTOREGRESSIVE MODEL;

D O I：

10.2307/2290725

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this article we propose a new class of models for nonlinear time series analysis, investigate properties of the proposed model, and suggest a modeling procedure for building such a model. The proPosed modeling procedure makes use of ideas from both parametric and nonparametric statistics, A consistency result is given to support the procedure. For illustration we apply the proposed model and procedure to several data sets and show that the resulting models substantially improve postsample multi-step ahead forecasts over other models.

引用

页码：298 / 308

页数：11

共 25 条

[1] NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].