Inference on Nonstationary Time Series with Moving Mean

Jiti Gao and Peter M. Robinson ()

No 15/13, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: A semiparametric 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 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. Unit root tests with standard asymptotics are thereby justified. Estimation of the trend function is also considered. We include a Monte Carlo study of finite-sample performance.

Keywords: fractional time series; fixed design nonparametric regression; non-stationary time series; unit root tests. (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: INFERENCE ON NONSTATIONARY TIME SERIES WITH MOVING MEAN (2016)
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