Report NEP-ECM-2022-12-05

This is the archive for NEP-ECM, a report on new working papers in the area of Econometrics. Sune Karlsson issued this report. It is usually issued weekly.

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Other reports in NEP-ECM

The following items were announced in this report:

• Karsten Reichold, 2022. "A Residuals-Based Nonparametric Variance Ratio Test for Cointegration," Papers 2211.06288, arXiv.org, revised Dec 2022.
• Anastasia Semykina, 2022. "Estimating Heterogeneous Effects in Static Binary Response Panel Data Models," Working Papers wp2022_11_01, Department of Economics, Florida State University.
• Ziyu Wang & Yucen Luo & Yueru Li & Jun Zhu & Bernhard Scholkopf, 2022. "Spectral Representation Learning for Conditional Moment Models," Papers 2210.16525, arXiv.org, revised Dec 2022.
• Richard Post & Isabel van den Heuvel & Marko Petkovic & Edwin van den Heuvel, 2022. "Flexible machine learning estimation of conditional average treatment effects: a blessing and a curse," Papers 2210.16547, arXiv.org, revised Jul 2023.
• Eric Gautier & Christiern Rose, 2022. "Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments," Papers 2211.02249, arXiv.org, revised Nov 2022.
• Paulo M.M. Rodrigues & Matei Demetrescu, 2022. "Cross-Sectional Error Dependence in Panel Quantile Regressions," Working Papers w202213, Banco de Portugal, Economics and Research Department.
• Kotlyarova, Yulia & Schafgans, Marcia M.A. & Zinde-Walsh, Victoria, 2021. "Rates of expansions for functional estimators," LSE Research Online Documents on Economics 113436, London School of Economics and Political Science, LSE Library.
• John Cai & Weinan Wang, 2022. "A Systematic Paradigm for Detecting, Surfacing, and Characterizing Heterogeneous Treatment Effects (HTE)," Papers 2211.01547, arXiv.org.
• Takuya Ishihara & Daisuke Kurisu, 2022. "Shrinkage Methods for Treatment Choice," Papers 2210.17063, arXiv.org, revised Jun 2024.
• Chris Muris & Konstantin Wacker, 2022. "Estimating interaction effects with panel data," Papers 2211.01557, arXiv.org.
• Bansak, Kirk & Nowacki, Tobias, 2022. "Effect Heterogeneity and Causal Attribution in Regression Discontinuity Designs," SocArXiv vj34m, Center for Open Science.
• Xiao Huang, 2022. "Boosted p-Values for High-Dimensional Vector Autoregression," Papers 2211.02215, arXiv.org, revised Mar 2023.
• Härtl, Tilmann, 2022. "Identifying Proxy VARs with Restrictions on the Forecast Error Variance," VfS Annual Conference 2022 (Basel): Big Data in Economics 264071, Verein für Socialpolitik / German Economic Association.
• Beiser-McGrath, Liam F., 2020. "Separation and rare events," LSE Research Online Documents on Economics 117222, London School of Economics and Political Science, LSE Library.
• Yuya Sasaki & Yulong Wang, 2022. "Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method," Papers 2210.16991, arXiv.org, revised Dec 2022.
• Tamimy, Zenab & van Bergen, Elsje & van der Zee, Matthijs D. & Dolan, Conor V. & Nivard, Michel Guillaume, 2022. "Multi Co-Moment Structural Equation Models: Discovering Direction of Causality in the Presence of Confounding," SocArXiv ynam2, Center for Open Science.
• Tzougas, George & Makariou, Despoina, 2022. "The multivariate Poisson-Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters," LSE Research Online Documents on Economics 117197, London School of Economics and Political Science, LSE Library.
• Toru Kitagawa & Hugo Lopez & Jeff Rowley, 2022. "Stochastic Treatment Choice with Empirical Welfare Updating," Papers 2211.01537, arXiv.org, revised Feb 2023.
• Fève, Patrick & Beaudry, Paul & Collard, Fabrice & Guay, Alain & Portier, Franck, 2022. "Dynamic Identification in VARs," TSE Working Papers 22-1384, Toulouse School of Economics (TSE).
• Maciej Wysocki & Paweł Sakowski, 2022. "Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models," Working Papers 2022-12, Faculty of Economic Sciences, University of Warsaw.
• Darjus Hosszejni & Sylvia Fruhwirth-Schnatter, 2022. "Cover It Up! Bipartite Graphs Uncover Identifiability in Sparse Factor Analysis," Papers 2211.00671, arXiv.org, revised Nov 2022.