Censored Nonparametric Time-Series Analysis with Autoregressive Error Models

Dursun Aydin () and Ersin Yilmaz ()
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Dursun Aydin: Mugla Sitki Kocman University
Ersin Yilmaz: Mugla Sitki Kocman University

Computational Economics, 2021, vol. 58, issue 2, No 1, 169-202

Abstract: Abstract This paper focuses on nonparametric regression modeling of time-series observations with data irregularities, such as censoring due to a cutoff value. In general, researchers do not prefer to put up with censored cases in time-series analyses because their results are generally biased. In this paper, we present an imputation algorithm for handling auto-correlated censored data based on a class of autoregressive nonparametric time-series model. The algorithm provides an estimation of the parameters by imputing the censored values with the values from a truncated normal distribution, and it enables unobservable values of the response variable. In this sense, the censored time-series observations are analyzed by nonparametric smoothing techniques instead of the usual parametric methods to reduce modelling bias. Typically, the smoothing methods are updated for estimating the censored time-series observations. We use Monte Carlo simulations based on right-censored data to compare the performances and accuracy of the estimates from the smoothing methods. Finally, the smoothing methods are illustrated using a meteorological time- series and unemployment datasets, where the observations are subject to the detection limit of the recording tool.

Keywords: Censored time series; Penalized spline; Smoothing spline; Auto-correlated data; Imputation method (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10614-020-10010-8

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