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Keywords:Bayesian inference 

Report
Sparse Trend Estimation

The low-frequency movements of many economic variables play a prominent role in policy analysis anddecision-making. We develop a robust estimation approach for these slow-moving trend processes, whichis guided by a judicious choice of priors and is characterized by sparsity. We present some novel stylizedfacts from longer-run survey expectations that inform the structure of the estimation procedure. Thegeneral version of the proposed Bayesian estimator with a slab-and-spike prior accounts explicitly forcyclical dynamics. The practical implementation of the method is discussed in detail, and ...
Staff Reports , Paper 1049

Working Paper
Facts and Fiction in Oil Market Modeling

A series of recent articles has called into question the validity of VAR models of the global market for crude oil. These studies seek to replace existing oil market models by structural VAR models of their own based on different data, different identifying assumptions, and a different econometric approach. Their main aim has been to revise the consensus in the literature that oil demand shocks are a more important determinant of oil price fluctuations than oil supply shocks. Substantial progress has been made in recent years in sorting out the pros and cons of the underlying econometric ...
Working Papers , Paper 1907

Working Paper
Refining the Workhorse Oil Market Model

The Kilian and Murphy (2014) structural vector autoregressive model has become the workhorse model for the analysis of oil markets. I explore various refinements and extensions of this model, including the effects of (1) correcting an error in the measure of global real economic activity, (2) explicitly incorporating narrative sign restrictions into the estimation, (3) relaxing the upper bound on the impact price elasticity of oil supply, (4) evaluating the implied posterior distribution of the structural models, and (5) extending the sample. I demonstrate that the substantive conclusions of ...
Working Papers , Paper 1910

Working Paper
Understanding the Estimation of Oil Demand and Oil Supply Elasticities

This paper examines the advantages and drawbacks of alternative methods of estimating oil supply and oil demand elasticities and of incorporating this information into structural VAR models. I not only summarize the state of the literature, but also draw attention to a number of econometric problems that have been overlooked in this literature. Once these problems are recognized, seemingly conflicting conclusions in the recent literature can be resolved. My analysis reaffirms the conclusion that the one-month oil supply elasticity is close to zero, which implies that oil demand shocks are the ...
Working Papers , Paper 2027

Working Paper
Assessing Regulatory Responses to Banking Crises

During banking crises, regulators must decide between bailouts or liquidations, neither of which are publicly popular. However, making a comprehensive assessment of regulators requires examining all their decisions against their dual objectives of preserving financial stability and discouraging moral hazard. I develop a Bayesian latent class model to assess regulators on these competing objectives and evaluate banking and savings and loan (S&L) regulators during the 1980s crises. I find that the banking authority (FDIC) conformed to these objectives whereas the S&L regulator (FSLIC), which ...
Research Working Paper , Paper RWP 22-04

Report
Estimating HANK for Central Banks

We provide a toolkit for efficient online estimation of heterogeneous agent (HA) New Keynesian (NK) models based on Sequential Monte Carlo methods. We use this toolkit to compare the out-of-sample forecasting accuracy of a prominent HANK model, Bayer et al. (2022), to that of the representative agent (RA) NK model of Smets and Wouters (2007, SW). We find that HANK’s accuracy for real activity variables is notably inferior to that of SW. The results for consumption in particular are disappointing since the main difference between RANK and HANK is the replacement of the RA Euler equation with ...
Staff Reports , Paper 1071

Working Paper
Bayesian Estimation and Comparison of Conditional Moment Models

We provide a Bayesian analysis of models in which the unknown distribution of the outcomes is speci?ed up to a set of conditional moment restrictions. This analysis is based on the nonparametric exponentially tilted empirical likelihood (ETEL) function, which is constructed to satisfy a sequence of unconditional moments, obtained from the conditional moments by an increasing (in sample size) vector of approximating functions (such as tensor splines based on the splines of each conditioning variable). The posterior distribution is shown to satisfy the Bernstein-von Mises theorem, subject to a ...
Working Papers , Paper 19-51

Working Paper
Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic

In this paper we resuscitate the mixed-frequency vector autoregression (MF-VAR) developed in Schorfheide and Song (2015) to generate real-time macroeconomic forecasts for the U.S. during the COVID-19 pandemic. The model combines eleven time series observed at two frequencies: quarterly and monthly. We deliberately do not modify the model specification in view of the recession induced by the COVID-19 outbreak. We find that forecasts based on a pre-crisis estimate of the VAR using data up until the end of 2019 appear to be more stable and reasonable than forecasts based on a sequence of ...
Working Papers , Paper 20-26

Report
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
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, document the accuracy and runtime benefits o fgeneralized data tempering for “online” estimation (that is, re-estimating a model asnew data become available), 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 and study the sensitivity ofthe predictive performance to ...
Finance and Economics Discussion Series , Paper 2020-023

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