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Working Paper
Learning About Consumer Uncertainty from Qualitative Surveys: As Uncertain As Ever
Sharp, Robert; Sarte, Pierre-Daniel G.; Pinto, Santiago (2015-09-03)
We study diffusion indices constructed from qualitative surveys to provide real-time assessments of various aspects of economic activity. In particular, we highlight the role of diffusion indices as estimates of change in a quasi extensive margin, and characterize their distribution, focusing on the uncertainty implied by both sampling and the polarization of participants' responses. Because qualitative tendency surveys generally cover multiple questions around a topic, a key aspect of this uncertainty concerns the coincidence of responses, or the degree to which polarization comoves, across ...
Working Paper , Paper 15-9

Working Paper
The Impact of Market Factors on Racial Identity: Evidence from Multiracial Survey Respondents
Jackson, Osborne (2024-10-01)
This paper examines the reported race of multiracial persons in the US Current Population Survey (CPS) before 2003, when limited response options exogenously constrained respondents to identify as a single race. Using this survey attribute and the 16-month longitudinal design of the basic monthly CPS, I explore whether market factors help causally determine racial identity. I find that pre-2003 race responds to state-level (1) racial composition, due largely to household composition, and (2) unemployment rates and wages by race. Although these findings suggest potential endogeneity of race, ...
Working Papers , Paper 24-13

Working Paper
Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach
Bognanni, Mark; Herbst, Edward (2015-12-18)
Vector autoregressions with Markov-switching parameters (MS-VARs) fit the data better than do their constant-parameter predecessors. However, Bayesian inference for MS-VARs with existing algorithms remains challenging. For our first contribution, we show that Sequential Monte Carlo (SMC) estimators accurately estimate Bayesian MS-VAR posteriors. Relative to multi-step, model-specific MCMC routines, SMC has the advantages of generality, parallelizability, and freedom from reliance on particular analytical relationships between prior and likelihood. For our second contribution, we use SMC's ...
Finance and Economics Discussion Series , Paper 2015-116

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
Simpler Bootstrap Estimation of the Asymptotic Variance of U-statistic Based Estimators
Hu, Luojia; Honore, Bo E. (2015-09-15)
The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it requires repeated re-calculation of the estimator. In Honor and Hu (2015), we propose a computationally simpler bootstrap procedure based on repeated re-calculation of one-dimensional estimators. The applicability of that approach is quite general. In this paper, we propose an alternative method which is specific to extremum estimators based on U-statistics. ...
Working Paper Series , Paper WP-2015-7

Working Paper
Robust Bayesian Analysis for Econometrics
Giacomini, Raffaella; Read, Matthew; Kitagawa, Toru (2021-08-23)
We review the literature on robust Bayesian analysis as a tool for global sensitivity analysis and for statistical decision-making under ambiguity. We discuss the methods proposed in the literature, including the different ways of constructing the set of priors that are the key input of the robust Bayesian analysis. We consider both a general set-up for Bayesian statistical decisions and inference and the special case of set-identified structural models. We provide new results that can be used to derive and compute the set of posterior moments for sensitivity analysis and to compute the ...
Working Paper Series , Paper WP-2021-11

Report
OLS Limit Theory for Drifting Sequences of Parameters on the Explosive Side of Unity
Magdalinos, Tassos; Petrova, Katerina (2024-08-01)
A limit theory is developed for the least squares estimator for mildly and purely explosive autoregressions under drifting sequences of parameters with autoregressive roots ρn satisfyingρn → ρ ∈ (—∞, —1] ∪ [1, ∞) and n (|ρn| —1) → ∞.Drifting sequences of innovations and initial conditions are also considered. A standard specification of a short memory linear process for the autoregressive innovations is extended to a triangular array formulation both for the deterministic weights and for the primitive innovations of the linear process, which are allowed to be ...
Staff Reports , Paper 1113

Working Paper
Aggregation level in stress testing models
McCarthy, Erin; Hale, Galina; Krainer, John (2015-09-28)
We explore the question of optimal aggregation level for stress testing models when the stress test is specified in terms of aggregate macroeconomic variables, but the underlying performance data are available at a loan level. Using standard model performance measures, we ask whether it is better to formulate models at a disaggregated level (?bottom up?) and then aggregate the predictions in order to obtain portfolio loss values or is it better to work directly with aggregated models (?top down?) for portfolio loss forecasts. We study this question for a large portfolio of home equity lines ...
Working Paper Series , Paper 2015-14

Working Paper
Estimating Dynamic Macroeconomic Models : How Informative Are the Data?
Beltran, Daniel O.; Draper, David (2016-08)
Central banks have long used dynamic stochastic general equilibrium (DSGE) models, which are typically estimated using Bayesian techniques, to inform key policy decisions. This paper offers an empirical strategy that quantifies the information content of the data relative to that of the prior distribution. Using an off-the-shelf DSGE model applied to quarterly Euro Area data from 1970:3 to 2009:4, we show how Monte Carlo simulations can reveal parameters for which the model's structure obscures identification. By integrating out components of the likelihood function and conducting a Bayesian ...
International Finance Discussion Papers , Paper 1175

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