We consider the extent to which different time-series models can generate simulated data with the same business cycle features that are evident in U.S. real GDP. We focus our analysis on whether multivariate linear models can improve on the previously documented failure of univariate linear...

We propose the use of likelihood-based confidence sets for the timing of structural breaks in parameters from time series regression models. The confidence sets are valid for the broad setting of a system of multivariate linear regression equations under fairly general assumptions about the...

We investigate the persistence of real exchange rates using Bayesian methods. First, an algorithm for Bayesian estimation of nonlinear threshold models is developed. Unlike standard grid-based estimation, the Bayesian approach fully captures joint parameter uncertainty and uncertainty about...

We consider which factors determined the price-rent ratio for the housing market in 18 U.S. metropolitan areas (MSAs) and at the national level over the period of 1975 to 2012. Based on a present-value framework, our proposed empirical model separates the price-rent ratio for a given market into...

We consider a time-varying parameter vector autoregressive model with stochastic volatility and mixture innovations to study the empirical relevance of the Lucas critique for the postwar U.S. economy. The model allows blocks of parameters to change at endogenously-estimated points of time....