We derive restrictions for Granger noncausality in Markov-switching vector autoregressive models and also show under which conditions a variable does not affect the forecast of the hidden Markov process. Based on Bayesian approach to evaluating the hypotheses, the computational tools for...

Methods for Bayesian testing and assessment of dynamic quantile forecasts are proposed. Specifically, Bayes factor analogues of popular frequentist tests for independence of violations from, and for correct coverage of a time series of, quantile forecasts are developed. To evaluate the relevant...

This paper aims to detect the presence of local non-stationarity of nonlinear autoregressive processes with heteroskedastic errors. A Bayesian test is developed to test for the unit root in multi-regime threshold autoregression with heteroskedasticity. To implement a test, a posterior odds...