We implement in the R package carx a novel and computationally efficient quasi-likelihood method for estimating a censored autoregressive model with exogenous covariates. The proposed quasi-likelihood method reduces to maximum likelihood estimation in absence of censoring. The carx package contains many useful functions for practical data analysis with censored stochastic regression, including functions for outlier detection, model diagnostics, and prediction with censored time series data. We illustrate the capabilities of the carx package with simulations and an elaborate real data analysis.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2017-064.zip
censReg, AER, NADA, VGAM, MCMCpack, cents, ARCensReg, carx, xts, TSA
Survival, TimeSeries, Econometrics, Distributions, Multivariate, Psychometrics, Bayesian, Environmetrics, ExtremeValue, Finance, SocialSciences, SpatioTemporal
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For attribution, please cite this work as
Wang & Chan, "The R Journal: carx: an R Package to Estimate Censored Autoregressive Time Series with Exogenous Covariates ", The R Journal, 2017
BibTeX citation
@article{RJ-2017-064,
author = {Wang, Chao and Chan, Kung-Sik},
title = {The R Journal: carx: an R Package to Estimate Censored Autoregressive Time Series with Exogenous Covariates },
journal = {The R Journal},
year = {2017},
note = {https://doi.org/10.32614/RJ-2017-064},
doi = {10.32614/RJ-2017-064},
volume = {9},
issue = {2},
issn = {2073-4859},
pages = {213-231}
}