INFERENCE ON NONSTATIONARY TIME SERIES WITH MOVING MEAN

被引：1
作者：
Gao, Jiti
Robinson, Peter M.
机构：
[1] Monash Univ, Clayton, Vic 3800, Australia
[2] London Sch Econ, London WC2A 2AE, England
来源：
ECONOMETRIC THEORY | 2016年 / 32卷 / 02期
基金：
澳大利亚研究理事会;
关键词：
FIXED-DESIGN REGRESSION; LONG-MEMORY ERRORS; NONPARAMETRIC REGRESSION; MODELS; TRENDS; TESTS;
D O I：
10.1017/S0266466614000875
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
A semiparametric model is proposed in which a parametric filtering of a nonstationary time series, incorporating fractionally differencing with short memory correction, removes correlation but leaves a nonparametric deterministic trend. Estimates of the memory parameter and other dependence parameters are proposed, and shown to be consistent and asymptotically normally distributed with parametric rate. Tests with standard asymptotics for I (1) and other hypotheses are thereby justified. Estimation of the trend function is also considered. We include a Monte Carlo study of finite-sample performance.
引用
收藏
页码：431 / 457
页数：27
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