ESTIMATION OF MISSING VALUES FOR BILINEAR TIME SERIES MODELS WITH GARCH INNOVATIONS USING NONPARAMETRIC METHODS

Abstract

A time series is defined as data recorded sequentially over a specified period.

Since the data are records taken overtime, missing observations in time series are very common. They may occur as a result of lost records, deletion of outliers, calender effects and defective measuring instruments Imputation is a necessary part of preprocessing of time series data