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dedup_wf_001--30c077d2fc8c7cca7a8ed3c9be419230
We study herein an autoregressive model with spatially correlated error terms and missing data. A logistic regression model with completely observed covariates is used to model... -
Volatility Martingale Difference Divergence Matrix and Its Application to Dim...
In this article, we propose the so-called volatility martingale difference divergence matrix (VMDDM) to quantify the conditional variance dependence of a random vector Y∈Rp... -
Default Correlations and Large-Portfolio Credit Analysis
A factor model with sparsely correlated residuals is used to model short-term probabilities of default and other corporate exits while permitting missing data, and serves as the... -
Testing for Uncorrelated Residuals in Dynamic Count Models With an Applicatio...
This article proposes new model checks for dynamic count models. Both portmanteau and omnibus-type tests for lack of residual autocorrelation are considered. The resulting test... -
Multiple regression model averaging and the focused information criterion wit...
We consider multiple regression (MR) model averaging using the Focused Informati on Criterion (FIC). Our approach is motivated by the problem of implementing a mean-variance... -
Micro-Level Estimation of Optimal Consumption Choice With Intertemporal Nonse...
This article investigates the presence of habit formation in household consumption, using data from the Panel Study of Income Dynamics. We develop an econometric model of... -
dedup_wf_001--104bd21b45fed5b4f4998c5a7bb60fb2
A common approach to evaluating robustness to omitted variable bias is to observe coefficient movements after inclusion of controls. This is informative only if selection on... -
Single index based CoVaR with very high dimensional covariates
Systemic risk analysis reveals the interdependencies of risk factors especially in tail event situations. In applications the focus of interest is on capturing joint tail... -
Threshold Regression With a Threshold Boundary
This article studies computation, estimation, inference, and testing for linearity in threshold regression with a threshold boundary. We first put forward a new algorithm to... -
Predicting Early Data Revisions to US GDP and the Effects of Releases on Equi...
The effects of data uncertainty on real-time decision-making can be reduced by predicting data revisions to U.S. GDP growth. We show that survey forecasts efficiently predict... -
dedup_wf_001--a02747351e4270df1c632532230d4782
We investigate the semiparametric smooth coefficient stochastic frontier model for panel data in which the distribution of the composite error term is assumed to be of known... -
dedup_wf_001--fb6a32a16af5f4eb48235f765bf30689
© 2018, American Statistical Association. We consider a statistical model for directed network formation that features both node-specific parameters that capture degree... -
Leverage, Asymmetry, and Heavy Tails in the High-Dimensional Factor Stochasti...
We develop a flexible modeling and estimation framework for a high-dimensional factor stochastic volatility (SV) model. Our specification allows for leverage effects, asymmetry... -
Words are the New Numbers: A Newsy Coincident Index of the Business Cycle
I construct a daily business cycle index based on quarterly GDP growth and textual information contained in a daily business newspaper. The newspaper data are decomposed into... -
dedup_wf_001--19af8529ec986c726fa0421aaaad4f03
This article proposes a new class of copula-based dynamic models for high-dimensional conditional distributions, facilitating the estimation of a wide variety of measures of...
