Report NEP-ECM-2023-04-03

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.

Subscribe to this report: email, RSS, or Mastodon.

Other reports in NEP-ECM

The following items were announced in this report:

• Seoyun Hong, 2023. "Censored Quantile Regression with Many Controls," Papers 2303.02784, arXiv.org.
• Otsu, Taisuke & Takahata, Keisuke & Xu, Mengshan, 2023. "Empirical likelihood inference for monotone index model," LSE Research Online Documents on Economics 118123, London School of Economics and Political Science, LSE Library.
• Abdul-Nasah Soale & Emmanuel Selorm Tsyawo, 2023. "Clustered Covariate Regression," Papers 2302.09255, arXiv.org, revised Jul 2023.
• Gordon Burtch & Edward McFowland III & Mochen Yang & Gediminas Adomavicius, 2023. "EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference," Papers 2303.02820, arXiv.org.
• Jin Seo Cho & Peter C. B. Phillips & Juwon Seo, 2023. "Functional Data Inference in a Parametric Quantile Model applied to Lifetime Income Curves," Working papers 2023rwp-211, Yonsei University, Yonsei Economics Research Institute.
• KANO, Takashi, 2023. "Posterior Inferences on Incomplete Structural Models : The Minimal Econometric Interpretation," Discussion paper series HIAS-E-128, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
• Quinn Lanners & Harsh Parikh & Alexander Volfovsky & Cynthia Rudin & David Page, 2023. "Variable Importance Matching for Causal Inference," Papers 2302.11715, arXiv.org, revised Jun 2023.
• Stommes, Drew & Aronow, P. M. & Sävje, Fredrik, 2023. "On the Reliability of Published Findings Using the Regression Discontinuity Design in Political Science," I4R Discussion Paper Series 22, The Institute for Replication (I4R).
• Chen Liu & Chao Wang & Minh-Ngoc Tran & Robert Kohn, 2023. "Deep Learning Enhanced Realized GARCH," Papers 2302.08002, arXiv.org, revised Oct 2023.
• Marzio Di Vece & Diego Garlaschelli & Tiziano Squartini, 2023. "Deterministic, quenched and annealed parameter estimation for heterogeneous network models," Papers 2303.02716, arXiv.org, revised Nov 2023.
• Riani, Marco & Atkinson, Anthony C. & Corbellini, Aldo & Farcomeni, Alessio & Laurini, Fabrizio, 2022. "Information criteria for outlier detection avoiding arbitrary significance levels," LSE Research Online Documents on Economics 113647, London School of Economics and Political Science, LSE Library.
• Sobin Joseph & Shashi Jain, 2023. "A neural network based model for multi-dimensional nonlinear Hawkes processes," Papers 2303.03073, arXiv.org.
• Cai, Hanqing & Wang, Tengyao, 2023. "Estimation of high-dimensional change-points under a group sparsity structure," LSE Research Online Documents on Economics 118366, London School of Economics and Political Science, LSE Library.
• Bonev, Petyo, 2023. "Behavioral Spillovers," Economics Working Paper Series 2303, University of St. Gallen, School of Economics and Political Science.
• Sam Dannels, 2023. "Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data," Papers 2302.10490, arXiv.org.
• Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
• Lin Liu & Chang Li, 2023. "New $\sqrt{n}$-consistent, numerically stable higher-order influence function estimators," Papers 2302.08097, arXiv.org.
• Hilde C. Bjornland & Yoosoon Chang & Jamie L. Cross, 2023. "Oil and the Stock Market Revisited: A Mixed Functional VAR Approach," CAEPR Working Papers 2023-005 Classification-1, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.