Bayesian Unit Root Test for AR(1) Model with Trend Approximated by Linear Spline Function
Overview
Performs Bayesian unit root testing for autoregressive time series models with non-linear trend components approximated by linear spline functions, as proposed by Kumar et al. (2020) doi:10.19139/soic-2310-5070-786. The package 'BayesSplineUR' computes posterior odds ratios, Bayes factors, and posterior probabilities for the unit root hypothesis against trend-stationary alternatives in models with linear spline trends or maintained polynomial trends as developed by Chaturvedi and Kumar (2005) doi:10.1016/j.spl.2005.04.044. Includes automatic knot selection using information criteria (AIC/BIC) and theoretical foundations for Bayesian unit root testing under structural breaks and maintained trends drawing from Schotman and van Dijk (1991) doi:10.1016/0304-4076(91)90038-F, Phillips and Perron (1988) doi:10.1093/biomet/75.2.335, Ouliaris et al. (1988) doi:10.1007/978-94-009-2953-1_10, and Perron (1989) doi:10.2307/1913683.
Install
Health
- OK13 OKNow
- OK7 OK2026-08-07
Documentation
- Help pages
- 9 (1 internal)
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 75%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 44%
Downloads
Dependencies
Installing BayesSplineUR adds no packages beyond those that come with R.
Code & Tests
Datasets
People & History
- 0.1.0Latest2026-08-06 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-08-06
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 4.6 KB / 2 files
- Download size
- 36 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("BayesSplineUR")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1016/j.spl.2005.04.044.
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