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Author:Schorfheide, Frank 

Online Estimation of DSGE Models

This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for ?online? estimation, and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts of DSGE models with and without financial frictions and document the benefits of conditioning DSGE model forecasts on nowcasts ...
Staff Reports , Paper 893

Working Paper
Assessing DSGE model nonlinearities

We develop a new class of nonlinear time-series models to identify nonlinearities in the data and to evaluate nonlinear DSGE models. U.S. output growth and the federal funds rate display nonlinear conditional mean dynamics, while inflation and nominal wage growth feature conditional heteroskedasticity. We estimate a DSGE model with asymmetric wage/price adjustment costs and use predictive checks to assess its ability to account for nonlinearities. While it is able to match the nonlinear inflation and wage dynamics, thanks to the estimated downward wage/price rigidities, these do not spill ...
Working Papers , Paper 13-47

Working Paper
Identifying long-run risks: a bayesian mixed-frequency approach

We develop a nonlinear state-space model that captures the joint dynamics of consumption, dividend growth, and asset returns. Building on Bansal and Yaron (2004), our model consists of an economy containing a common predictable component for consumption and dividend growth and multiple stochastic volatility processes. The estimation is based on annual consumption data from 1929 to 1959, monthly consumption data after 1959, and monthly asset return data throughout. We maximize the span of the sample to recover the predictable component and use high-frequency data, whenever available, to ...
Working Papers , Paper 13-39

Working Paper
On the fit and forecasting performance of New Keynesian models

The paper provides new tools for the evaluation of DSGE models and applies them to a large-scale New Keynesian dynamic stochastic general equilibrium (DSGE) model with price and wage stickiness and capital accumulation. Specifically, we approximate the DSGE model by a vector autoregression (VAR) and then systematically relax the implied cross-equation restrictions. Let --denote the extent to which the restrictions are being relaxed. We document how the in- and out-of-sample fit of the resulting specification (DSGE-VAR) changes as a function of --. Furthermore, we learn about the precise ...
FRB Atlanta Working Paper , Paper 2004-37

Discussion Paper
Why Didn’t Inflation Collapse in the Great Recession?

GDP contracted 4 percent from 2008:Q2 to 2009:Q2, and the unemployment rate peaked at 10 percent in October 2010. Traditional backward-looking Phillips curve models of inflation, which relate inflation to measures of “slack” in activity and past measures of inflation, would have predicted a substantial drop in inflation. However, core inflation declined by only one percentage point, from 2.2 percent in 2007 to 1.2 percent in 2009, giving rise to the “missing deflation” puzzle. Based on this evidence, some authors have argued that slack must have been smaller than suggested by ...
Liberty Street Economics , Paper 20140813

Working Paper
Tempered Particle Filtering

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 straightforward to implement, the practical performance is often poor. We develop a self-tuning particle filter in which the proposal distribution is constructed adaptively through a sequence of Monte Carlo steps. Intuitively, we start from a measurement error distribution with an inflated variance, and then ...
Finance and Economics Discussion Series , Paper 2016-072

Working Paper
Sequential Monte Carlo sampling for DSGE models

We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models, wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using three examples consisting of an artificial state-space model, the Smets and Wouters (2007) model, and Schmitt-Grohe and Uribe's (2012) news shock model we show that the SMC algorithm is better suited for multi-modal and irregular posterior distributions than the widely-used random walk Metropolis-Hastings algorithm. Unlike standard Markov chain Monte Carlo ...
Working Papers , Paper 12-27

Working Paper
Improving GDP measurement: a forecast combination perspective

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 GDI . The authors propose and explore a "forecast combination" approach to combining them. They then put the theory to work, producing a superior combined estimate of GDP growth for the U.S., GDPC. The authors compare GDPC to GDPE and GDPI , with particular attention to behavior over the business cycle. They discuss several variations and extensions.
Working Papers , Paper 11-41

Methods versus substance: measuring the effects of technology shocks on hours

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 strategies and not by the statistical approaches. The crucial parameter is the labor supply elasticity. Both a calibration procedure that uses modern assessments of the Frisch elasticity and the estimation procedures result in technology shocks accounting for 2% to 9% of the variation in hours worked in the ...
Staff Report , Paper 433

Working Paper
Improving GDP measurement: a measurement-error perspective

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 measure and lead to substantially-revised views regarding the properties of GDP.
Working Papers , Paper 13-16


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