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INFERENCE ON NONSTATIONARY TIME SERIES WITH MOVING MEAN

Jiti Gao () and Peter M. Robinson

Econometric Theory, 2016, vol. 32, issue 2, 431-457

Abstract: 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.

Date: 2016
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Working Paper: Inference on Nonstationary Time Series with Moving Mean (2013) Downloads
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