Bayesian Unit Root Test for AR(1) Model with Trend Approximated by Linear Spline Function

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) . 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) . 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) , Phillips and Perron (1988) , Ouliaris et al. (1988) , and Perron (1989) .