This paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not explicitly appear in the model (non-core variables). We use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to...

In this paper, we employ both calibration and modern (Bayesian) estimation methods to assess the role of neutral and investment-specific technology shocks in generating fluctuations in hours. Using a neoclassical stochastic growth model, we show how answers are shaped by the identification...

The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t-1 particle values into time t values. In the widely-used bootstrap particle filter, this distribution is generated by the state-transition equation. While...

This paper considers the problem of forecasting a collection of short time series using cross sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This...

This paper assesses biases in policy predictions due to the lack of invariance of "structural'' parameters in representative-agent models. We simulate data under various fiscal policy regimes from a heterogeneous-agents economy with incomplete asset markets and indivisible labor supply....

Two often-divergent U.S. GDP estimates are available, a widely-used expenditure side version, GDPE, and a much less widely-used income-side version GDPI . We propose and explore a "forecast combination" approach to combining them. We then put the theory to work, producing a superior combined...

There is a fast growing literature that partially identifies structural vector autoregressions (SVARs) by imposing sign restrictions on the responses of a subset of the endogenous variables to a particular structural shock (sign-restricted SVARs). To date, the methods that have been used are...

We propose and illustrate a Markov-switching multi-fractal duration (MSMD) model for analysis of inter-trade durations in financial markets. We establish several of its key properties with emphasis on high persistence (indeed long memory). Empirical exploration suggests MSMD's superiority...

We provide a new and superior measure of U.S. GDP, obtained by applying optimal signal-extraction techniques to the (noisy) expenditure-side and income-side estimates. Its properties - particularly as regards serial correlation - differ markedly from those of the standard expenditure-side...

This paper develops a vector autoregression (VAR) for time series which are observed at mixed frequencies - quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to...