We show that the use of prior information derived from former empirical findings and/or subject matter theory regarding the lag structure of the observable variables together with an AR process for the error terms can produce univariate and single equation models that are intuitively appealing,...

This paper shows how the dynamic linear model with fixed regressors can be efficiently estimated. This dynamic model can be used to distinguish spurious correlation from state dependence and we show that the integrated likelihood estimator is adaptive for any asymptotics with T increasing where...

We develop a DSGE model in which the policy rate signals the central bank.s view about macroeconomic developments to incompletely informed price setters. The model is estimated with likelihood methods on a U.S. data set including the Survey of Professional Forecasters as a measure of price...

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, we use nonparametric Bayesian methods to flexibly model the skewness and...

We examine the sources of macroeconomic economic fluctuations by estimating a variety of medium-scale DSGE models within a unified framework that incorporates regime switching both in shock variances and in the inflation target. Our general framework includes a number of different model features...

This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature, the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given...

In this paper, we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional,...

In systems of variables with a specified or already identified cointegrating rank, stationarity of component variates can be tested by a simple restriction test. The implied decision is often in conflict with the outcome of unit root tests on the same variables. Using a framework of Bayes...

Assuming a normal-Wishart modelling framework we compare two methods for finding outliers in a multivariate regression (MR) system. One method is the add-1-dummy approach which needs fewer parameters and a model choice criterion while the other method estimates the outlier probability for each...

Bivariate time series data often show strong relationships between the two components, while both individual variables can be approximated by random walks in the short run and are obviously bounded in the long run. Three model classes are considered for a time-series model selection problem:...