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Report NEP-ECM-2026-06-15
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, or Bluesky.
Other reports in NEP-ECM
The following items were announced in this report:
- Martin Huber, 2026, "When Do Treatment Changes Identify Causal Effects?," Papers, arXiv.org, number 2606.02234, Jun, revised Jun 2026.
- Yuxin Tao & Feiyu Jiang & Xiaofeng Shao, 2026, "Generalized Spectral Testing with Sample Splitting," Papers, arXiv.org, number 2605.29315, May.
- Shakeeb Khan & Tatiana Komarova & Denis Nekipelov, 2026, "Random Set Quantile Estimation of Partially Identified Discrete Response Models," Papers, arXiv.org, number 2606.02200, Jun.
- Silvia Goncalves & Ana Maria Herrera & Lutz Kilian & Elena Peavento & Iones Kelanemer Holban, 2026, "Semiparametric Local Projections," Papers, arXiv.org, number 2606.13519, Jun.
- Todd E. Clark & Florian Huber & Gary Koop, 2026, "A Nonparametric Approach to Augmenting a Bayesian VAR with Nonlinear Factors," Working Papers, Federal Reserve Bank of Cleveland, number 26-14, Jun, DOI: 10.26509/frbc-wp-202614.
- Yulin Zhang & Lin Liu & Zheng Zhang, 2026, "Higher-Order Debiased Estimators for General Treatment Models," Papers, arXiv.org, number 2606.01706, Jun, revised Jun 2026.
- Ashraf, Shaista & Shah, Ismail & Javed, Farrukh, 2026, "Functional Penalized Ridge Regression with a Parametric Partitioning Framework for High-Dimensional Data," MPRA Paper, University Library of Munich, Germany, number 127993, Feb.
- Masahiro Kato, 2026, "Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression," Papers, arXiv.org, number 2606.12892, Jun.
- David Van Dijcke & Kaspar Wuthrich, 2026, "IV regression with distribution-valued outcomes," Papers, arXiv.org, number 2605.28749, May.
- Lianyan Fu & Rui Wang & Zihan Zhang, 2026, "Graph Neural Networks for Generalized Mundlak Estimator under Network Confounding," Papers, arXiv.org, number 2605.29238, May.
- Zhiruo Zhang & Firmin Doko Tchatoka & Qazi Haque, 2026, "Adaptive Bayesian Shrinkage of High-Dimensional Panel VARs," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-40, Jun.
- Zhenshan Chen & Klaus Moeltner & Matthew Mair, 2026, "Recovering Direct Price Effects of Environmental Amenities in Housing Markets: Regression and Causal Machine Learning Model Assessment with Empirical Monte Carlo Simulation," Papers, arXiv.org, number 2606.02795, Jun.
- Naoya Nagasaka, 2026, "Identifying Macro Shocks from Micro Evidence: A Mixed Autoregressive Approach," Discussion Paper Series, Research Institute for Economics & Business Administration, Kobe University, number DP2026-18, Jun.
- Conte, Anna & Hey, John D., 2026, "REDRUM: Robust Estimation and Design for the Random Utility Model," MPRA Paper, University Library of Munich, Germany, number 129162, May.
- Yutong Chao & Resat Gokhan & Jalal Etesami & Ali Habibnia, 2026, "Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data," Papers, arXiv.org, number 2605.26271, May.
- Victor Duarte & Julia Fonseca, 2026, "AI for Structural Estimation," NBER Working Papers, National Bureau of Economic Research, Inc, number 35283, May.
- Chunrong Ai & Zeqi Wu & Zheng Zhang, 2026, "Data-Automated Policy Learning for Nonlinear Welfare," Papers, arXiv.org, number 2606.01659, Jun.
- David Staines, 2026, "Statistical and Numerical Convergence in Stochastic Equilibrium," Papers, arXiv.org, number 2606.07469, Jun, revised Jun 2026.
- Pinjaman, Saizal, 2026, "A Simple Note on Augmented Autoregressive Distributed Lag Model (A-ARDL)," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 341087, DOI: 10.6084/m9.figshare.32304789.