We introduce a Bayesian approach to model assessment in the class of graphical vector autoregressive (VAR) processes. Due to the very large number of model structures that may be considered, simulation based inference, such as Markov chain Monte Carlo, is not feasible. Therefore, we derive an...

This is a simulation-based warning note for practitioners who use the M GLS MGLS unit root tests in the context of structural change using different selection lag length criteria. With T=100 T=100 , we find severe oversize problems when using some criteria, while other criteria produce an...

We provide a simulation smoother to a exible state-space model with lagged states and lagged dependent variables. Qian (2014) has introduced this state-space model and proposes a fast Kalman filter with time-varying state dimension in the presence of missing observations in the data. In this...

This paper introduces a novel way of differentiating a unit root from a stationary alternative. We write up the model consisting of zero and nonzero parameters. If the lagged dependent variable has a coefficient of zero, we know that the variable has a unit root. We exploit this property and...

A nonparametric version of the Final Prediction Error (FPE) is proposed for lag selection in nonlinear autoregressive time series. We derive its consistency for both local constant and local linear estimators using a derived optimal bandwidth. Further asymptotic analysis suggests a greater...

This paper examines the impacts of two attributes of accounting recognition, i.e., nonlinearity and multi-period lags, on Basu (1997) measure of asymmetric timeliness. First, we predict and find that asymmetric timeliness is affected by nonlinear earnings responses and more pronounced when news...