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Working Paper
The Distributional Predictive Content of Measures of Inflation Expectations
Mitchell, James; Zaman, Saeed (2023-11-30)
This paper examines the predictive relationship between the distribution of realized inflation in the US and measures of inflation expectations from households, firms, financial markets, and professional forecasters. To allow for nonlinearities in the predictive relationship we use quantile regression methods. We find that the ability of households to predict future inflation, relative to that of professionals, firms, and the market, increases with inflation. While professional forecasters are more accurate in the middle of the inflation density, households’ expectations are more useful in ...
Working Papers , Paper 23-31

Working Paper
Proxy SVARs: Asymptotic Theory, Bootstrap Inference, and the Effects of Income Tax Changes in the United States
Lunsford, Kurt Graden; Jentsch, Carsen (2016-07-19)
Proxy structural vector autoregressions (SVARs) identify structural shocks in vector autoregressions (VARs) with external proxy variables that are correlated with the structural shocks of interest but uncorrelated with other structural shocks. We provide asymptotic theory for proxy SVARs when the VAR innovations and proxy variables are jointly ?-mixing. We also prove the asymptotic validity of a residual-based moving block bootstrap (MBB) for inference on statistics that depend jointly on estimators for the VAR coefficients and for covariances of the VAR innovations and proxy variables. These ...
Working Papers (Old Series) , Paper 1619

Working Paper
Inference in Bayesian Proxy-SVARs
Rubio-Ramirez, Juan F.; Waggoner, Daniel F.; Arias, Jonas E. (2021-01-14)
Motivated by the increasing use of external instruments to identify structural vector autoregressions (SVARs), we develop an algorithm for exact finite sample inference in this class of time series models, commonly known as Proxy-SVARs. Our algorithm makes independent draws from any posterior distribution over the structural parameterization of a Proxy-SVAR. Our approach allows researchers to simultaneously use proxies and traditional zero and sign restrictions to identify structural shocks. We illustrate our methods with two applications. In particular, we show how to generalize the ...
FRB Atlanta Working Paper , Paper 2018-16a

Working Paper
Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach
Herbst, Edward P.; Bognanni, Mark (2014-11-12)
Vector autoregressions with Markov-switching parameters (MS-VARs) offer dramatically better data fit than their constant-parameter predecessors. However, computational complications, as well as negative results about the importance of switching in parameters other than shock variances, have caused MS-VARs to see only sparse usage. For our first contribution, we document the effectiveness of Sequential Monte Carlo (SMC) algorithms at estimating MSVAR posteriors. Relative to multi-step, model-specific MCMC routines, SMC has the advantages of being simpler to implement, readily parallelizable, ...
Working Papers (Old Series) , Paper 1427

Working Paper
Explaining Machine Learning by Bootstrapping Partial Dependence Functions and Shapley Values
Palmer, Nathan M.; Modig, Zach; Gupton, Greg; Cook, Thomas R. (2021-11-15)
Machine learning and artificial intelligence methods are often referred to as “black boxes” when compared with traditional regression-based approaches. However, both traditional and machine learning methods are concerned with modeling the joint distribution between endogenous (target) and exogenous (input) variables. Where linear models describe the fitted relationship between the target and input variables via the slope of that relationship (coefficient estimates), the same fitted relationship can be described rigorously for any machine learning model by first-differencing the partial ...
Research Working Paper , Paper RWP 21-12

Working Paper
Improved Estimation of Poisson Rate Distributions through a Multi-Mode Survey Design
Hitczenko, Marcin (2021-02-03)
Researchers interested in studying the frequency of events or behaviors among a population must rely on count data provided by sampled individuals. Often, this involves a decision between live event counting, such as a behavioral diary, and recalled aggregate counts. Diaries are generally more accurate, but their greater cost and respondent burden generally yield less data. The choice of survey mode, therefore, involves a potential tradeoff between bias and variance of estimators. I use a case study comparing inferences about payment instrument use based on different survey designs to ...
FRB Atlanta Working Paper , Paper 2021-10

Working Paper
A staggered pricing approach to modeling speculative storage: implications for commodity price dynamics
Gospodinov, Nikolay; Dabbous, Amal; Assa, Hirbod (2013-09-01)
This paper embeds a staggered price feature into the standard speculative storage model of Deaton and Laroque (1996). Intermediate goods inventory speculators are added as an additional source of intertemporal linkage, which helps us to replicate the stylized facts of the observed commodity price dynamics. Incorporating this type of friction into the model is motivated by its ability to increase price stickiness which, gives rise to a higher degree of persistence in the first two conditional moments of commodity prices. The structural parameters of our model are estimated by the simulated ...
FRB Atlanta Working Paper , Paper 2013-08

Working Paper
Real-Time Density Nowcasts of US Inflation: A Model-Combination Approach
Knotek, Edward S.; Zaman, Saeed (2020-10-22)
We develop a flexible modeling framework to produce density nowcasts for US inflation at a trading-day frequency. Our framework: (1) combines individual density nowcasts from three classes of parsimonious mixed-frequency models; (2) adopts a novel flexible treatment in the use of the aggregation function; and (3) permits dynamic model averaging via the use of weights that are updated based on learning from past performance. Together these features provide density nowcasts that can accommodate non-Gaussian properties. We document the competitive properties of the nowcasts generated from our ...
Working Papers , Paper 20-31

Working Paper
Bayesian Estimation of Time-Changed Default Intensity Models
Gordy, Michael B.; Szerszen, Pawel J. (2015-01-06)
We estimate a reduced-form model of credit risk that incorporates stochastic volatility in default intensity via stochastic time-change. Our Bayesian MCMC estimation method overcomes nonlinearity in the measurement equation and state-dependent volatility in the state equation. We implement on firm-level time-series of CDS spreads, and find strong in-sample evidence of stochastic volatility in this market. Relative to the widely-used CIR model for the default intensity, we find that stochastic time-change offers modest benefit in fitting the cross-section of CDS spreads at each point in time, ...
Finance and Economics Discussion Series , Paper 2015-2

Working Paper
Empirical Bayes Control of the False Discovery Exceedance
Basu, Pallavi; Fu, Luella; Saretto, Alessio; Sun, Wenguang (2021-11-18)
In sparse large-scale testing problems where the false discovery proportion (FDP) is highly variable, the false discovery exceedance (FDX) provides a valuable alternative to the widely used false discovery rate (FDR). We develop an empirical Bayes approach to controlling the FDX. We show that for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to FDX ...
Working Papers , Paper 2115

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