Results 11 to 20 of about 628 (47)
On the Past, Present, and Future of the Diebold-Yilmaz Approach to Dynamic Network Connectedness
, 2022 We offer retrospective and prospective assessments of the Diebold-Yilmaz connectedness research program, combined with personal recollections of its development.
Diebold, Francis X., Yilmaz, Kamil
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Testing and correcting sample selection in academic achievement comparisons
, 2023 Country comparisons using standardized test scores may in some cases be misleading unless we make sure that the potential sample selection bias created by drop-outs and non-enrollment patterns does not alter the analysis.
Boussim, Onil
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Bandwidth Selection for Treatment Choice with Binary Outcomes
, 2023 This study considers the treatment choice problem when outcome variables are binary. We focus on statistical treatment rules that plug in fitted values based on nonparametric kernel regression and show that optimizing two parameters enables the ...
Ishihara, Takuya
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Finite Sample Performance of a Conduct Parameter Test in Homogenous Goods Markets
, 2023 We assess the finite sample performance of the conduct parameter test in homogeneous goods markets. Statistical power rises with an increase in the number of markets, a larger conduct parameter, and a stronger demand rotation instrument.
Matsumura, Yuri, Otani, Suguru
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, 2023 This paper develops a new toolbox for multiple structural break detection in panel data models with interactive effects. The toolbox includes tests for the presence of structural breaks, a break date estimator, and a break date confidence interval.
Ditzen, Jan +2 more
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Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling
, 2021 We develop a new method of online inference for a vector of parameters estimated by the Polyak-Ruppert averaging procedure of stochastic gradient descent (SGD) algorithms.
Lee, Sokbae +3 more
core +1 more source
DeepVol: A Deep Transfer Learning Approach for Universal Asset Volatility Modeling
, 2023 This paper introduces DeepVol, a promising new deep learning volatility model that outperforms traditional econometric models in terms of model generality.
Gerlach, Richard +4 more
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Smoothed instrumental variables quantile regression
, 2023 In this article, I introduce the sivqr command, which estimates the coefficients of the instrumental variables (IV) quantile regression model introduced by Chernozhukov and Hansen (2005).
Kaplan, David M.
core +1 more source
Cluster-Robust Inference Robust to Large Clusters
, 2023 The recent literature Sasaki and Wang (2022) points out that the conventional cluster-robust standard errors fail in the presence of large clusters.
Chiang, Harold D. +2 more
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Unified Inference for Dynamic Quantile Predictive Regression
, 2023 This paper develops unified asymptotic distribution theory for dynamic quantile predictive regressions which is useful when examining quantile predictability in stock returns under possible presence of nonstationarity.Comment: arXiv admin note: text ...
Katsouris, Christis
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