Computational Statistics & Data Analysis
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.
Publicado en: Computational Statistics & Data Analysis