Minimum distance estimation of locally stationary moving average processes

M. Ignacia Vicuña, Wilfredo Palma and Ricardo Olea

Computational Statistics & Data Analysis, 2019, vol. 140, issue C, 1-20

Abstract: The minimum distance methodology can be applied to the estimation of locally stationary moving average processes. This novel approach allows for the analysis of time series data exhibiting non-stationary behavior. The main advantages of this method are that it does not depend on the distribution of the process, can handle missing data and is computationally efficient. Some large sample properties of the new estimator are investigated, establishing its consistency and asymptotic normality. The Monte Carlo experiments presented show that the estimates behave well even for small sample sizes. The proposed methodology is illustrated by means of an application to a real-life time series of data.

Keywords: Local stationarity; Non-stationarity; Minimum distance estimation; Time-varying models (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:140:y:2019:i:c:p:1-20

DOI: 10.1016/j.csda.2019.05.005

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