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Working Paper
A New Way to Quantify the Effect of Uncertainty
Throckmorton, Nathaniel A.; Richter, Alexander W. (2017-05-04)
This paper develops a new way to quantify the effect of uncertainty and other higher-order moments. First, we estimate a nonlinear model using Bayesian methods with data on uncertainty, in addition to common macro time series. This key step allows us to decompose the exogenous and endogenous sources of uncertainty, calculate the effect of volatility following the cost of business cycles literature, and generate data-driven policy functions for any higherorder moment. Second, we use the Euler equation to analytically decompose consumption into several terms--expected consumption, the ex-ante ...
Working Papers , Paper 1705

Report
Estimating HANK for Central Banks
Acharya, Sushant; Chen, William; Del Negro, Marco; Dogra, Keshav; Gleich, Aidan; Goyal, Shlok; Matlin, Ethan; Lee, Donggyu; Sarfati, Reca; Sengupta, Sikata (2023-08-01)
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
Inference Based on Scale, Label, and Economic Restrictions
Arias, Jonas E.; Rubio-Ramirez, Juan F.; Waggoner, Daniel F. (2026-07-22)
The results of nearly 100 prominent studies in empirical macroeconomics have been called into question by Baumeister and Hamilton (2018). We show that their concern about distributional asymmetry for a typical question of interest under a uniform prior with respect to the Haar measure is actually driven by an unacknowledged sign restriction. We also demonstrate that such a prior induces symmetric prior distributions over individual impulse responses conditional on the reduced-form parameters, or more generally when the prior over the reduced-form covariance matrix rules out correlation among ...
Working Papers , Paper 26-36

Working Paper
Large Vector Autoregressions with Stochastic Volatility and Flexible Priors
Clark, Todd E.; Carriero, Andrea; Marcellino, Massimiliano (2016-06-30)
Recent research has shown that a reliable vector autoregressive model (VAR) for forecasting and structural analysis of macroeconomic data requires a large set of variables and modeling time variation in their volatilities. Yet, there are no papers jointly allowing for stochastic volatilities and large datasets, due to computational complexity. Moreover, homoskedastic VAR models for large datasets so far restrict substantially the allowed prior distributions on the parameters. In this paper we propose a new Bayesian estimation procedure for (possibly very large) VARs featuring time varying ...
Working Papers (Old Series) , Paper 1617

Working Paper
Indeterminacy and Imperfect Information
Matthes, Christian; Mertens, Elmar; Lubik, Thomas A. (2019-10-08)
We study equilibrium determination in an environment where two kinds of agents have different information sets: The fully informed agents know the structure of the model and observe histories of all exogenous and endogenous variables. The less informed agents observe only a strict subset of the full information set. All types of agents form expectations rationally, but agents with limited information need to solve a dynamic signal extraction problem to gather information about the variables they do not observe. We show that for parameter values that imply a unique equilibrium under full ...
Working Paper , Paper 19-17

Working Paper
Risks and Uncertainty in Monetary Policy
Adrian, Tobias; Giannone, Domenico; Luciani, Matteo; West, Mike (2026-09-01)
Central banks monitor macroeconomic risk through two traditions: scenario analysis, regularly used since the mid-1990s, and distributional forecasting, practiced since the late 1960s. The two are complementary but separate: scenarios provide narratives without probabilities, while predictive distributions provide probabilities with limited economic interpretation. Treating baseline forecasts and scenarios as conditional predictive densities, and distributional forecasts as reference predictive distributions, places both within a common framework and clarifies their roles. The Scenario ...
Finance and Economics Discussion Series , Paper 2026-061

Working Paper
Financial Frictions, Financial Shocks, and Aggregate Volatility
Fuentes-Albero, Cristina (2014-10)
I revisit the Great Inflation and the Great Moderation. I document an immoderation in corporate balance sheet variables so that the Great Moderation is best described as a period of divergent patterns in volatilities for real, nominal and financial variables. A model with time-varying financial frictions and financial shocks allowing for structural breaks in the size of shocks and the institutional framework is estimated. The paper shows that (i) while the Great Inflation was driven by bad luck, the Great Moderation is mostly due to better institutions; (ii) the slowdown in credit spreads is ...
Finance and Economics Discussion Series , Paper 2014-084

Working Paper
Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors
Jensen, Mark J.; Fisher, Mark (2018-02-01)
Change point models using hierarchical priors share in the information of each regime when estimating the parameter values of a regime. Because of this sharing, hierarchical priors have been very successful when estimating the parameter values of short-lived regimes and predicting the out-of-sample behavior of the regime parameters. However, the hierarchical priors have been parametric. Their parametric nature leads to global shrinkage that biases the estimates of the parameter coefficient of extraordinary regimes toward the value of the average regime. To overcome this shrinkage, we model ...
FRB Atlanta Working Paper , Paper 2018-2

Working Paper
Inference for Local Projections
Inoue, Atsushi; Jordà, Òscar; Kuersteiner, Guido M. (2024-08-13)
Inference for impulse responses estimated with local projections presents interesting challenges and opportunities. Analysts typically want to assess the precision of individual estimates, explore the dynamic evolution of the response over particular regions, and generally determine whether the impulse generates a response that is any different from the null of no effect. Each of these goals requires a different approach to inference. In this article, we provide an overview of results that have appeared in the literature in the past 20 years along with some new procedures that we introduce ...
Working Paper Series , Paper 2024-29

Working Paper
Uniform Priors for Impulse Responses
Rubio-Ramirez, Juan F.; Waggoner, Daniel F.; Arias, Jonas E. (2023-09-28)
There has been a call for caution when using the conventional method for Bayesian inference in set-identified structural vector autoregressions on the grounds that the uniform prior over the set of orthogonal matrices could be nonuniform for individual impulse responses or other quantity of interest. This paper challenges this call by formally showing that, when the focus is on joint inference, the uniform prior over the set of orthogonal matrices is not only sufficient but also necessary for inference based on a uniform joint prior distribution over the identified set for the vector of ...
FRB Atlanta Working Paper , Paper 2023-13

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