Report NEP-ECM-2021-12-20

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.

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Other reports in NEP-ECM

The following items were announced in this report:

• Hervé Cardot & Antonio Musolesi, 2021, "Zero-inflated regression for unobserved effects panel data models and difference-in-differences estimation," SEEDS Working Papers, SEEDS, Sustainability Environmental Economics and Dynamics Studies, number 1121, Dec, revised Dec 2021.
• Majed Dodin, 2021, "Optimized Inference in Regression Kink Designs," Papers, arXiv.org, number 2111.10713, Nov.
• Yuqian Zhang & Weijie Ji & Jelena Bradic, 2021, "Dynamic treatment effects: high-dimensional inference under model misspecification," Papers, arXiv.org, number 2111.06818, Nov, revised Jan 2025.
• Matthieu Garcin & Maxime L. D. Nicolas, 2021, "Nonparametric estimator of the tail dependence coefficient: balancing bias and variance," Papers, arXiv.org, number 2111.11128, Nov, revised Jul 2023.
• Xiu Xu & Weining Wang & Yongcheol Shin & Chaowen Zheng, 2021, "Dynamic Network Quantile Regression Model," Papers, arXiv.org, number 2111.07633, Nov.
• Taiga Tsubota, 2021, "Identifying Dynamic Discrete Choice Models with Hyperbolic Discounting," Papers, arXiv.org, number 2111.10721, Nov, revised Oct 2024.
• Joshua C. C. Chan & Gary Koop & Xuewen Yu, 2021, "Large Order-Invariant Bayesian VARs with Stochastic Volatility," Papers, arXiv.org, number 2111.07225, Nov.
• Max Cytrynbaum, 2021, "Optimal Stratification of Survey Experiments," Papers, arXiv.org, number 2111.08157, Nov, revised Aug 2023.
• Oscar Engelbrektson, 2021, "Why Synthetic Control estimators are biased and what to do about it: Introducing Relaxed and Penalized Synthetic Controls," Papers, arXiv.org, number 2111.10784, Nov.
• Junhui Cai & Dan Yang & Ran Chen & Wu Zhu & Haipeng Shen & Linda Zhao, 2021, "Network regression and supervised centrality estimation," Papers, arXiv.org, number 2111.12921, Nov, revised Feb 2025.
• Riccardo D'Adamo, 2021, "Orthogonal Policy Learning Under Ambiguity," Papers, arXiv.org, number 2111.10904, Nov, revised Dec 2022.
• Blankmeyer, Eric, 2021, "Peer Groups and Bias Detection in Least Squares Regression," MPRA Paper, University Library of Munich, Germany, number 110866, Nov.
• Xingwei Hu, 2021, "Decoding Causality by Fictitious VAR Modeling," Papers, arXiv.org, number 2111.07465, Nov, revised Nov 2021.
• Claudia Shi & Dhanya Sridhar & Vishal Misra & David M. Blei, 2021, "On the Assumptions of Synthetic Control Methods," Papers, arXiv.org, number 2112.05671, Dec, revised Dec 2021.
• Sokol, Andrej, 2021, "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series, European Central Bank, number 2624, Dec.
• Clements, Adam & Hurn, Stan & Volkov, Vladimir, 2021, "A simple linear alternative to multiplicative error models with an application to trading volume," Working Papers, University of Tasmania, Tasmanian School of Business and Economics, number 2021-06.
• Myoung-jae Lee & Sanghyeok Lee, 2021, "Difference in Differences and Ratio in Ratios for Limited Dependent Variables," Papers, arXiv.org, number 2111.12948, Nov, revised Aug 2023.
• Christian Bongiorno & Damien Challet & Gr'egoire Loeper, 2021, "Cleaning the covariance matrix of strongly nonstationary systems with time-independent eigenvalues," Papers, arXiv.org, number 2111.13109, Nov, revised Mar 2023.
• Mr. Jorge A Chan-Lau, 2020, "UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification," IMF Working Papers, International Monetary Fund, number 2020/262, Nov.
• Kenji Hatakenaka & Kosuke Oya, 2021, "Bayesian inference for time varying partial adjustment model with application to intraday price discovery," Discussion Papers in Economics and Business, Osaka University, Graduate School of Economics, number 21-19, Nov.
• Jonathan Roth & Guillaume Saint-Jacques & YinYin Yu, 2021, "An Outcome Test of Discrimination for Ranked Lists," Papers, arXiv.org, number 2111.07889, Nov.
• Luxuan Yang & Ting Gao & Yubin Lu & Jinqiao Duan & Tao Liu, 2021, "Neural network stochastic differential equation models with applications to financial data forecasting," Papers, arXiv.org, number 2111.13164, Nov, revised Nov 2022.