In this paper we investigate the forecasting performance of the median CPI in a variety of Bayesian VARs (BVARs) that are often used for monetary policy. Until now, the use of trimmed-mean price statistics in forecasting inflation has often been relegated to simple univariate or "Philips-Curve"...

Several Phillips curves based on sticky information and sticky prices are estimated and compared using Bayesian VAR-GMM. This method derives expectations in each Phillips curve from a VAR and estimates the Phillips curve parameters and the VAR coefficients simultaneously. Quasi-marginal...

With the concept of trend inflation now widely understood as to be important as a measure of the public's perception of the inflation goal of the central bank and important to the accuracy of longer-term inflation forecasts, this paper uses Bayesian methods to assess alternative models of trend...

A knowledge of the level of trend inflation is key to many current policy decisions, and several methods of estimating trend inflation exist. This paper adds to the growing literature which uses survey-based long-run forecasts of inflation to estimate trend inflation. We develop a bivariate...

A large literature has established that the Fed's change from a passive to an active policy response to inflation led to U.S. macroeconomic stability after the Great Inflation of the 1970s. This paper revisits the literature's view by estimating a generalized New Keynesian model using a...

This paper develops a new class of structural vector autoregressions (SVARs) with time-varying parameters, which I call a drifting SVAR (DSVAR). The DSVAR is the first structural time-varying parameter model to allow for internally consistent probabilistic inference under exact—or...

This paper shows entropic tilting to be a flexible and powerful tool for combining medium-term forecasts from BVARs with short-term forecasts from other sources (nowcasts from either surveys or other models). Tilting systematically improves the accuracy of both point and density forecasts, and...

We propose a new framework for measuring uncertainty and its effects on the economy, based on a large VAR model with errors whose stochastic volatility is driven by two common unobservable factors, representing aggregate macroeconomic and financial uncertainty. The uncertainty measures can also...

We develop novel multivariate time series models using Bayesian additive regression trees that posit nonlinear relationships among macroeconomic variables, their lags, and possibly the lags of the errors. The variance of the errors can be stable, driven by stochastic volatility (SV), or follow a...

This paper presents a 16-variable Bayesian VAR forecasting model of the U.S. economy for use in a monetary policy setting. The variables that comprise the model are selected not only for their effectiveness in forecasting the primary variables of interest, but also for their relevance to the...