Inference on Non-Stationary Time Series with Moving Mean

32 Pages Posted: 30 Jul 2013

See all articles by Jiti Gao

Jiti Gao

Monash University - Department of Econometrics & Business Statistics

Peter M. Robinson

London School of Economics & Political Science (LSE) - Department of Economics; National Bureau of Economic Research (NBER)

Date Written: July 23, 2013

Abstract

A semi-parametric model is proposed in which a parametric filtering of a non-stationary time series, incorporating fractionally differencing with short memory correction, removes correlation but leaves a non-parametric 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. Unit root tests with standard asymptotic distributions are thereby justified. Estimation of the trend function is also considered. We include a Monte Carlo study of …nite-sample performance.

Keywords: fractional time series, fi…xed design non-parametric regression, non-stationary time series, unit root tests

JEL Classification: C14, C22

Suggested Citation

Gao, Jiti and Robinson, Peter M., Inference on Non-Stationary Time Series with Moving Mean (July 23, 2013). Available at SSRN: https://ssrn.com/abstract=2302894 or http://dx.doi.org/10.2139/ssrn.2302894

Jiti Gao (Contact Author)

Monash University - Department of Econometrics & Business Statistics ( email )

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HOME PAGE: http://www.jitigao.com

Peter M. Robinson

London School of Economics & Political Science (LSE) - Department of Economics ( email )

Houghton Street
London WC2A 2AE
United Kingdom

National Bureau of Economic Research (NBER)

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Cambridge, MA 02138
United States

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