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Working Paper
Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach
Bognanni, Mark; Herbst, Edward (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
DSGE-SVt: An Econometric Toolkit for High-Dimensional DSGE Models with SV and t Errors
Shin, Minchul; Tan, Fei; Chib, Siddhartha (2021-01-04)
Currently, there is growing interest in dynamic stochastic general equilibrium (DSGE) models that have more parameters, endogenous variables, exogenous shocks, and observables than the Smets and Wouters (2007) model, and substantial additional complexities from non-Gaussian distributions and the incorporation of time-varying volatility. The popular DYNARE software package, which has proved useful for small and medium-scale models is, however, not capable of handling such models, thus inhibiting the formulation and estimation of more re-alistic DSGE models. A primary goal of this paper is to ...
Working Papers , Paper 21-02

Working Paper
Variable Annuities: Underlying Risks and Sensitivities
Chahboun, Imad; Hoover, Nathaniel (2019-04-09)
This paper presents a quantitative model designed to understand the sensitivity of variable annuity (VA) contracts to market and actuarial assumptions and how these sensitivities make them a potentially important source of risk to insurance companies during times of stress. VA contracts often include long dated guarantees of market performance that expose the insurer to multiple nondiversifiable risks. Our modeling framework employs a Monte Carlo simulation of asset returns and policyholder behavior to derive fair prices for variable annuities in a risk neutral framework and to estimate ...
Supervisory Research and Analysis Working Papers , Paper RPA 19-1

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
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
High-Dimensional DSGE Models: Pointers on Prior, Estimation, Comparison, and Prediction∗
Shin, Minchul; Tan, Fei; Chib, Siddhartha (2020-09-15)
Working Papers , Paper 20-35

Working Paper
Proxy SVARs: Asymptotic Theory, Bootstrap Inference, and the Effects of Income Tax Changes in the United States
Jentsch, Carsen; Lunsford, Kurt Graden (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
Easy Bootstrap-Like Estimation of Asymptotic Variances
Hu, Luojia; Honore, Bo E. (2018-06-29)
The bootstrap is a convenient tool for calculating standard errors of the parameter estimates of complicated econometric models. Unfortunately, the bootstrap can be very time-consuming. In a recent paper, Honor and Hu (2017), we propose a ?Poor (Wo)man's Bootstrap? based on one-dimensional estimators. In this paper, we propose a modified, simpler method and illustrate its potential for estimating asymptotic variances.
Working Paper Series , Paper WP-2018-11

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

Report
Deconstructing the yield curve
Gospodinov, Nikolay; Crump, Richard K. (2019-04-01)
We introduce a novel nonparametric bootstrap for assets with a finite maturity structure such as the nominal yield curve. We analyze the properties of our resampling procedure for inference on bond return predictability. Our method is asymptotically valid and robust to general forms of time and cross-sectional dependence; moreover, it exhibits excellent finite-sample properties. We demonstrate the applicability of our results in two empirical exercises: first, we show that a proxy for equity market tail risk predicts bond returns beyond yield curve factors; second, we provide a bootstrap bias ...
Staff Reports , Paper 884

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