Report NEP-ECM-2023-11-20

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:

• Brice Romuald Gueyap Kounga, 2023, "Nonparametric Regression with Dyadic Data," Papers, arXiv.org, number 2310.12825, Oct.
• Donald S. Poskitt & Xueyan Zhao, 2023, "Bootstrap Hausdorff Confidence Regions for Average Treatment Effect Identified Sets," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 9/23.
• Denis Chetverikov & Daniel Wilhelm, 2023, "Inference for Rank-Rank Regressions," Papers, arXiv.org, number 2310.15512, Oct, revised Jul 2025.
• Pan Zhao & Yifan Cui, 2023, "A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning," Papers, arXiv.org, number 2310.09545, Oct.
• Maciej Berk{e}sewicz, 2023, "Survey calibration for causal inference: a simple method to balance covariate distributions," Papers, arXiv.org, number 2310.11969, Oct, revised Mar 2024.
• B. Cooper Boniece & Jos'e E. Figueroa-L'opez & Yuchen Han, 2023, "Data-driven fixed-point tuning for truncated realized variations," Papers, arXiv.org, number 2311.00905, Nov, revised Oct 2024.
• Sid Kankanala, 2023, "On Gaussian Process Priors in Conditional Moment Restriction Models," Papers, arXiv.org, number 2311.00662, Nov, revised Nov 2023.
• Martin Magris & Alexandros Iosifidis, 2023, "Variational Inference for GARCH-family Models," Papers, arXiv.org, number 2310.03435, Oct.
• Yuta Okamoto, 2023, "Robustify and Tighten the Lee Bounds: A Sample Selection Model under Stochastic Monotonicity and Symmetry Assumptions," Papers, arXiv.org, number 2311.00439, Nov, revised Jan 2025.
• Isaiah Andrews & Nano Barahona & Matthew Gentzkow & Ashesh Rambachan & Jesse M. Shapiro, 2023, "Structural Estimation Under Misspecification: Theory and Implications for Practice," NBER Working Papers, National Bureau of Economic Research, Inc, number 31799, Oct.
• Ghislain Geniaux, 2023, "Functional gradient descent boosting for additive non‐linear spatial autoregressive model (gaussian and probit)," Post-Print, HAL, number hal-04229868, May.
• Holger Dette & Martin Schumann, 2023, "Testing for equivalence of pre-trends in Difference-in-Differences estimation," Papers, arXiv.org, number 2310.15796, Oct, revised Dec 2025.
• Donggyu Kim & Minseog Oh, 2023, "Dynamic Realized Minimum Variance Portfolio Models," Papers, arXiv.org, number 2310.13511, Oct.
• Dimitris Christopoulos & Peter McAdam & Elias Tzavalis, 2023, "Threshold Endogeneity in Threshold VARs: An Application to Monetary State Dependence," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 23-09, Jul, DOI: 10.18651/RWP2023-09.
• Julia Hatamyar & Noemi Kreif & Rudi Rocha & Martin Huber, 2023, "Machine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity," Papers, arXiv.org, number 2310.11962, Oct.
• Xin Du & Kai Moriyama & Kumiko Tanaka-Ishii, 2023, "Co-Training Realized Volatility Prediction Model with Neural Distributional Transformation," Papers, arXiv.org, number 2310.14536, Oct.
• Davide Viviano & Lihua Lei & Guido Imbens & Brian Karrer & Okke Schrijvers & Liang Shi, 2023, "Causal clustering: design of cluster experiments under network interference," Papers, arXiv.org, number 2310.14983, Oct, revised Jan 2025.
• Julio Gálvez, 2023, "Household portfolio choices under (non-)linear income risk: an empirical framework," Working Papers, Banco de España, number 2327, Sep, DOI: https://doi.org/10.53479/33792.
• HONDA, Toshio & 本田, 敏雄 & WU, Wei-Ying, 2024, "Sparse quantile regression via ℓ０-penalty," Discussion Papers, Graduate School of Economics, Hitotsubashi University, number 2023-03, Nov.
• Puwasala Gamakumara & Edgar Santos-Fernandez & Priyanga Dilini Talagala & Rob J Hyndman & Kerrie Mengersen & Catherine Leigh, 2023, "Conditional Normalization in Time Series Analysis," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 10/23.
• Matteo Barigozzi & Marc Hallin, 2023, "Dynamic Factor Models: a Genealogy," Working Papers ECARES, ULB -- Universite Libre de Bruxelles, number 2023-15, Oct.
• Geoffrey Barrows & Hélène Ollivier & Ariell Reshef, 2023, "Production Function Estimation with Multi-Destination Firms," CESifo Working Paper Series, CESifo, number 10716.
• John Mullahy, 2023, "Analyzing Bounded Count Data," NBER Working Papers, National Bureau of Economic Research, Inc, number 31814, Oct.
• Patrick Rehill & Nicholas Biddle, 2023, "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers, arXiv.org, number 2310.13240, Oct, revised Mar 2024.
• Joshua Chan, 2023, "BVARs and Stochastic Volatility," Papers, arXiv.org, number 2310.14438, Oct.
• Minglian Lin & Indranil SenGupta & William Wilson, 2023, "Estimation of VaR with jump process: application in corn and soybean markets," Papers, arXiv.org, number 2311.00832, Nov, revised Jun 2024.
• Joshua Rosaler & Dhruv Desai & Bhaskarjit Sarmah & Dimitrios Vamvourellis & Deran Onay & Dhagash Mehta & Stefano Pasquali, 2023, "Enhanced Local Explainability and Trust Scores with Random Forest Proximities," Papers, arXiv.org, number 2310.12428, Oct, revised Aug 2024.

Report NEP-ECM-2023-11-20

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: