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Jel Classification:C15 

A Bayesian Approach to Inference on Probabilistic Surveys

We propose a nonparametric Bayesian approach for conducting inference on probabilistic surveys. We use this approach to study whether U.S. Survey of Professional Forecasters density projections for output growth and inflation are consistent with the noisy rational expectations hypothesis. We find that in contrast to theory, for horizons close to two years, there is no relationship whatsoever between subjective uncertainty and forecast accuracy for output growth density projections, both across forecasters and over time, and only a mild relationship for inflation projections. As the horizon ...
Staff Reports , Paper 1025

Working Paper
exuber: Recursive Right-Tailed Unit Root Testing with R

This paper introduces the R package exuber for testing and date-stamping periods of mildly explosive dynamics (exuberance) in time series. The package computes test statistics for the supremum ADF test (SADF) of Phillips, Wu and Yu (2011), the generalized SADF (GSADF) of Phillips, Shi and Yu (2015a,b), and the panel GSADF proposed by Pavlidis, Yusupova, Paya, Peel, Martínez-García, Mack and Grossman (2016); generates finite-sample critical values based on Monte Carlo and bootstrap methods; and implements the corresponding date-stamping procedures. The recursive least-squares algorithm that ...
Globalization Institute Working Papers , Paper 383

Working Paper
Mobility and Engagement Following the SARS-Cov-2 Outbreak

We develop a Mobility and Engagement Index (MEI) based on a range of mobility metrics from Safegraph geolocation data, and validate the index with mobility data from Google and Unacast. We construct MEIs at the county, MSA, state and nationwide level, and link these measures to indicators of economic activity. According to our measures, the bulk of sheltering-in-place and social disengagement occurred during the week of March 15 and simultaneously across the U.S. At the national peak of the decline in mobility in early April, localities that engaged in a 10% larger decrease in mobility than ...
Working Papers , Paper 2014

Working Paper
Measuring Transaction Costs in the Absence of Timestamps

This paper develops measures of transaction costs in the absence of transaction timestamps and information about who initiates transactions, which are data limitations that often arise in studies of over-the-counter markets. I propose new measures of the effective spread and study the performance of all estimators analytically, in simulations, and present an empirical illustration with small-cap stocks for the 2005-2014 period. My theoretical, simulation, and empirical results provide new insights into measuring transaction costs and may help guide future empirical work.
Finance and Economics Discussion Series , Paper 2017-045

Working Paper
A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains

This paper proposes a moment-matching method for approximating vector autoregressions by finite-state Markov chains. The Markov chain is constructed by targeting the conditional moments of the underlying continuous process. The proposed method is more robust to the number of discrete values and tends to outperform the existing methods for approximating multivariate processes over a wide range of the parameter space, especially for highly persistent vector autoregressions with roots near the unit circle.
FRB Atlanta Working Paper , Paper 2013-05

Working Paper
Proxy SVARs: Asymptotic Theory, Bootstrap Inference, and the Effects of Income Tax Changes in the United States

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
Tempered Particle Filtering

The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t-1 particle values into time t values. In the widely-used bootstrap particle filter this distribution is generated by the state-transition equation. While straightforward to implement, the practical performance is often poor. We develop a self-tuning particle filter in which the proposal distribution is constructed adaptively through a sequence of Monte Carlo steps. Intuitively, we start from a measurement error distribution with an inflated variance, and then ...
Finance and Economics Discussion Series , Paper 2016-072

Working Paper
Complementarity and Macroeconomic Uncertainty

Macroeconomic uncertainty—the conditional volatility of the unforecastable component of a future value of a time series—shows considerable variation in the data. A typical assumption in business cycle models is that production is Cobb-Douglas. Under that assumption, this paper shows there is usually little, if any, endogenous variation in output uncertainty, and first moment shocks have similar effects in all states of the economy. When the model departs from Cobb-Douglas production and assumes capital and labor are gross complements, first-moment shocks have state-dependent effects and ...
Working Papers , Paper 2009

Working Paper
Explaining Machine Learning by Bootstrapping Partial Dependence Functions and Shapley Values

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

Deconstructing the yield curve

We introduce a novel nonparametric bootstrap for the nominal yield curve which is agnostic to the true factor structure. We deconstruct the yield curve into primitive objects, with weak cross-sectional and time-series dependence, which serve as building blocks for resampling the data. We analyze the asymptotic and finite-sample properties of the bootstrap for mimicking salient features of the data and conducting inference on bond return predictability. We demonstrate the applicability of our results to: the “tent shape” in forward rates, regression tests of the expectations hypothesis, ...
Staff Reports , Paper 884


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