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...

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...

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...

Macroeconomic risk assessments play an important role in the forecasts of many institutions. A risk forecast is related to the potential asymmetry of the forecast density. In this work, we investigate how the optimality of such risk forecasts can be tested. We find that the Pearson mode skewness...

Under the Basel II regulatory framework non-negligible statistical problems arise when backtesting risk measures. In this setting backtests often become infeasible due to a low number of violations leading to heavy size distortions. According to Escanciano and Olmo (2010, 2011) these problems...