Report NEP-ECM-2023-02-27

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:

• Harold D Chiang & Yukitoshi Matsushita & Taisuke Otsu, 2023. "Regression adjustment in randomized controlled trials with many covariates," STICERD - Econometrics Paper Series 627, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
• Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Inference in Non-stationary High-Dimensional VARs," Papers 2302.01434, arXiv.org, revised Sep 2023.
• Liudas Giraitis & Yufei Li & Peter C.B. Phillips, 2023. "Robust Inference on Correlation under General Heterogeneity," Cowles Foundation Discussion Papers 2354, Cowles Foundation for Research in Economics, Yale University.
• Pesaran, M. H. & Smith, R. P., 2023. "The Role of Pricing Errors in Linear Asset Pricing Models with Strong, Semi-strong, and Latent Factors," Cambridge Working Papers in Economics 2317, Faculty of Economics, University of Cambridge.
• Nicholas Brown, 2023. "Moment-Based Estimation of Linear Panel Data Models with Factor-Augmented Errors," Working Paper 1498, Economics Department, Queen's University.
• Chronopoulos, Ilias & Raftapostolos, Aristeidis & Kapetanios, George, 2023. "Forecasting Value-at-Risk using deep neural network quantile regression," Essex Finance Centre Working Papers 34837, University of Essex, Essex Business School.
• Jan Pruser & Florian Huber, 2023. "Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions," Papers 2301.13604, arXiv.org, revised Sep 2023.
• Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org, revised Nov 2024.
• Cheng Peng & Stanislav Uryasev, 2023. "Factor Model of Mixtures," Papers 2301.13843, arXiv.org, revised Mar 2023.
• Rainer Winkelmann, 2023. "Neglected heterogeneity, Simpson’s paradox, and the anatomy of least squares," ECON - Working Papers 426, Department of Economics - University of Zurich, revised Jul 2023.
• Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.
• Harold D Chiang & Yuya Sasaki, 2023. "On Using The Two-Way Cluster-Robust Standard Errors," Papers 2301.13775, arXiv.org.
• Jean-Jacques Forneron, 2023. "Noisy, Non-Smooth, Non-Convex Estimation of Moment Condition Models," Papers 2301.07196, arXiv.org, revised Feb 2023.
• Geert Dhaene & Martin Weidner, 2023. "Approximate Functional Differencing," Papers 2301.13736, arXiv.org, revised May 2023.
• Richard K. Crump & Nikolay Gospodinov & Hunter Wieman, 2023. "Sparse Trend Estimation," Staff Reports 1049, Federal Reserve Bank of New York.
• Bo E. Honore & Luojia Hu, 2021. "Sample Selection Models Without Exclusion Restrictions: Parameter Heterogeneity and Partial Identification," Working Paper Series WP 2022-33, Federal Reserve Bank of Chicago.
• Phuong Anh Nguyen & Michael Wolf, 2023. "Single-firm inference in event studies via the permutation test," ECON - Working Papers 425, Department of Economics - University of Zurich, revised Nov 2023.
• Benjamin Avanzi & Greg Taylor & Melantha Wang & Bernard Wong, 2023. "Machine Learning with High-Cardinality Categorical Features in Actuarial Applications," Papers 2301.12710, arXiv.org.
• Matteo Barigozzi & Filippo Pellegrino, 2023. "Multidimensional dynamic factor models," Papers 2301.12499, arXiv.org.
• Mario Forni & Luca Gambetti & Giovanni Ricco, 2023. "External Instrument SVAR Analysis forNoninvertible Shocks," Working Papers 2023-03, Center for Research in Economics and Statistics.