Vector autoregressions with Markov-switching parameters (MS-VARs) fit the data better than do their constant-parameter predecessors. However, Bayesian inference for MS-VARs with existing algorithms remains challenging. For our first contribution, we show that Sequential Monte Carlo (SMC)...

Modeling interest rates over samples that include the Great Recession requires taking stock of the effective lower bound (ELB) on nominal interest rates. We propose a flexible time– series approach which includes a “shadow rate”—a notional rate that is less than the ELB during the period...

This paper proposes a multivariate stochastic volatility-in-vector autoregression model called the conditional autoregressive inverse Wishart-in-VAR (CAIW-in-VAR) model as a framework for studying the real effects of uncertainty shocks. We make three contributions to the literature. First, the...

Considerable attention has been devoted to evaluating the macroeconomic effectiveness of the Federal Reserve's communications about future policy rates (forward guidance) in light of the U.S. economy's long spell at the zero lower bound (ZLB). In this paper, we study whether forward guidance...

U.S. estimates of the natural rate of interest – the real short-term interest rate that would prevail absent transitory disturbances – have declined dramatically since the start of the global financial crisis. For example, estimates using the Laubach-Williams (2003) model indicate the...

Money markets have been operating under a new monetary policy implementation framework since the Federal Reserve started paying interest on bank reserves in late 2008. The regulatory environment has also evolved substantially over this period. We develop and test hypotheses regarding the effects...

In this paper, we examine the results of GDP trend-cycle decompositions from the estimation of bivariate unobserved components models that allow for correlated trend and cycle innovations. Three competing variables are considered in the bivariate setup along with GDP: the unemployment rate, the...

The period of extraordinary volatility in euro area headline inflation starting in 2007 raised the question whether forecast combination methods can be used to hedge against bad forecast performance of single models during such periods and provide more robust forecasts. We investigate this issue...

Applied researchers interested in estimating key parameters of DSGE models face an array of choices regarding numerical solution and estimation methods. We focus on the likelihood evaluation of models with occasionally binding constraints. We document how solution approximation errors and...

Factor models are widely used in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. In these models, the reduction of the predictors and the modeling and forecasting of the response y are carried out in two...