In this paper, we propose a fully Bayesian approach to the special class of nonlinear time-series models called the logistic smooth transition autoregressive (LSTAR) model. Initially, a Gibbs sampler is proposed for the LSTAR where the lag length, k, is kept fixed. Then, uncertainty about k is...

We explore some aspects of the analysis of latent component structure in non-stationary time series based on time-varying autoregressive (TVAR) models that incorporate uncertainty on model order. Our modelling approach assumes that the AR coefficients evolve in time according to a random walk...

Can information on macroeconomic uncertainty improve the forecast accuracy for key macroeconomic time series for the US? Since previous studies have demonstrated that the link between the real economy and uncertainty is subject to nonlinearities, I assess the predictive power of macroeconomic...

Households face earnings risk which is non-normal and varies by age and over the income distribution. We show that allowing for these rich features of earnings dynamics, in the context of a structurally estimated life-cycle portfolio choice model, helps to rationalize the limited participation...

This paper examines if overreaction of oil price forecasters is related to uncertainty. Furthermore, it takes into account impacts from oil price return and oil price volatility on forecast changes. The panel smooth transition regression model from González et al. (2005) is applied with...