Censored Nonparametric Time-Series Analysis with Autoregressive Error Models

• Published in: Computational economics Vol. 58; no. 2; pp. 169 - 202
• Main Authors: Aydin, Dursun, Yilmaz, Ersin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2021; Springer Nature B.V
• Subjects: Algorithms; Autoregressive models; Behavioral/Experimental Economics; Bias; Computer Appl. in Social and Behavioral Sciences; Datasets; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Error analysis; Imputing; Math Applications in Computer Science; Methods; Monte Carlo simulation; Nonparametric statistics; Normal distribution; Operations Research/Decision Theory; Parameter estimation; Random variables; Smoothing; Statistical analysis; Time series; Unemployment; Values; Auto-correlated data; Penalized spline; Imputation method; Censored time series; Smoothing spline
• ISSN: 0927-7099; 1572-9974
• DOI: 10.1007/s10614-020-10010-8

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.