| Type: | Package |
| Title: | Bayesian Unit Root Test for Model with Maintained Trend |
| Version: | 0.1.0 |
| Description: | Performs Bayesian unit root testing for time series models with maintained polynomial trend components as proposed by Chaturvedi and Kumar (2005) <doi:10.1016/j.spl.2005.04.044>. The package 'BayesURTrend' computes posterior odds ratios, Bayes factors, and posterior probabilities for unit root hypotheses against stationary alternatives in autoregressive models augmented with polynomial trends. Methodological foundations for Bayesian unit root testing under structural breaks and maintained trends are drawn 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>, and Ouliaris et al. (1988) <doi:10.1007/978-94-009-2953-1_10>. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.3 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, graphics |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2026-07-28 03:12:30 UTC; shikhar tyagi |
| Author: | Shikhar Tyagi 
[aut, cre],
Arvind Pandey [aut],
Bhupendra Singh [aut],
Vrijesh Tripathi [aut] |
| Maintainer: | Shikhar Tyagi <shikhar1093tyagi@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-08-06 07:00:02 UTC |
Bayesian Unit Root Test for Model with Maintained Trend
Description
Computes the posterior odds ratio, Bayes factor, and posterior probabilities for testing the unit root hypothesis in an autoregressive model with maintained polynomial trend, as proposed by Chaturvedi and Kumar (2005).
Usage
bayes_ur_test(y, p = 1, k = 1, a = 0, p0 = 0.5, V = NULL, n_grid = 200)
chaturvedi_test(y, p = 1, k = 1, a = 0, p0 = 0.5, V = NULL, n_grid = 200)
Arguments
y |
A numeric vector or univariate time series. |
p |
Non-negative integer specifying the degree of the polynomial trend (default is 1 for linear trend). |
k |
Non-negative integer specifying the order of augmentation / lagged differences (default is 1). |
a |
Lower bound of the prior interval (a, 1) for autoregressive parameter rho under H1 (default is 0). Must satisfy -1 < a < 1. |
p0 |
Prior probability of the unit root hypothesis H0 (default is 0.5). Must satisfy 0 < p0 < 1. |
V |
Optional prior precision matrix for trend coefficients mu (p x p matrix) or scalar scaling parameter (default is 1e-4 * I_p). |
n_grid |
Integer specifying the number of grid points for evaluating posterior density of rho under H1 (default is 200). |
Value
An object of class "bayes_ur_test" and "htest" containing:
statistic |
The posterior odds ratio (b01) in favor of the unit root hypothesis H0. |
p.value |
The posterior probability P(H0|y) of the unit root hypothesis. |
bayes_factor |
The Bayes factor (BF01) in favor of H0 relative to H1. |
posterior_h0 |
Posterior probability of the unit root hypothesis H0. |
posterior_h1 |
Posterior probability of the stationary alternative hypothesis H1. |
rho_summary |
Vector of summary statistics for rho under H1 (mean, sd, median, 2.5%, 97.5% quantiles). |
rho_grid |
Vector of evaluation grid points for rho in (a, 1). |
rho_density |
Vector of normalized posterior density values for rho under H1. |
parameter |
Named vector of test settings (p, k, a, p0). |
method |
Character string describing the test method. |
data.name |
Character string providing the data name. |
estimates |
Named vector of estimated parameter values. |
References
Chaturvedi, A., & Kumar, J. (2005). Bayesian unit root test for model with maintained trend. Statistics & Probability Letters, 74(1), 109–115. doi:10.1016/j.spl.2005.04.044
Schotman, P., & van Dijk, H. K. (1991). A Bayesian analysis of the unit root in real exchange rates. Journal of Econometrics, 49(1-2), 195–238. doi:10.1016/0304-4076(91)90038-F
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. doi:10.1093/biomet/75.2.335
Examples
set.seed(123)
# Simulated random walk (Unit root process)
y_rw <- cumsum(rnorm(60))
res_rw <- bayes_ur_test(y_rw, p = 1, k = 1)
print(res_rw)
summary(res_rw)
# Stationary AR(1) process
y_stat <- numeric(60)
for (t in 2:60) y_stat[t] <- 0.5 * y_stat[t - 1] + rnorm(1)
res_stat <- bayes_ur_test(y_stat, p = 1, k = 1)
print(res_stat)
Simulated Macroeconomic Time Series Data
Description
A simulated univariate time series vector containing 100 observations exhibiting a random walk with drift, suitable for demonstrating unit root testing.
Usage
macro_data
Format
A numeric vector of length 100.
Source
Simulated dataset generated for package documentation and examples.
Examples
data(macro_data)
res <- bayes_ur_test(macro_data, p = 1, k = 1)
print(res)
Plot Method for Bayesian Unit Root Test
Description
Plots the posterior density of the autoregressive parameter rho under H1 along with prior density and unit root probability.
Usage
## S3 method for class 'bayes_ur_test'
plot(x, ...)
Arguments
x |
An object of class |
... |
Further graphical arguments. |
Value
No return value, called for side effects (plotting).
Print Method for Bayesian Unit Root Test
Description
Prints a concise summary of the Bayesian unit root test results.
Usage
## S3 method for class 'bayes_ur_test'
print(x, ...)
Arguments
x |
An object of class |
... |
Further arguments passed to or from other methods. |
Value
Invisibly returns the input object x.
Summary Method for Bayesian Unit Root Test
Description
Provides detailed summary output for a Bayesian unit root test object.
Usage
## S3 method for class 'bayes_ur_test'
summary(object, ...)
Arguments
object |
An object of class |
... |
Further arguments passed to or from other methods. |
Value
Invisibly returns the input object object.