Report NEP-ECM-2024-09-23

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:

• Ying Zeng, 2024. "Estimation and Inference of Average Treatment Effect in Percentage Points under Heterogeneity," Papers 2408.06624, arXiv.org.
• Stephan Hetzenecker & Maximilian Osterhaus, 2024. "Deep Learning for the Estimation of Heterogeneous Parameters in Discrete Choice Models," Papers 2408.09560, arXiv.org.
• Wenjie Wang & Yichong Zhang, 2024. "Gradient Wild Bootstrap for Instrumental Variable Quantile Regressions with Weak and Few Clusters," Papers 2408.10686, arXiv.org.
• Alexander Mayer & Dominik Wied, 2024. "Endogeneity Corrections in Binary Outcome Models with Nonlinear Transformations: Identification and Inference," Papers 2408.06977, arXiv.org, revised Nov 2024.
• Cong Wang, 2024. "Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis," Papers 2408.09271, arXiv.org, revised Sep 2024.
• Abhinandan Dalal & Patrick Blobaum & Shiva Kasiviswanathan & Aaditya Ramdas, 2024. "Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters," Papers 2408.09598, arXiv.org, revised Sep 2024.
• Lucas Zhang, 2024. "Continuous difference-in-differences with double/debiased machine learning," Papers 2408.10509, arXiv.org.
• Guanghui Pan, 2024. "Methodological Foundations of Modern Causal Inference in Social Science Research," Papers 2408.00032, arXiv.org.
• Bhattacharjee, A. & Ditzen, J., & Holly, S., 2024. "Engle-Granger Representation in Spatial and Spatio-Temporal Models," Cambridge Working Papers in Economics 2447, Faculty of Economics, University of Cambridge.
• Harvey, A. & Simons, J., 2024. "Hidden Threshold Models with applications to asymmetric cycles," Cambridge Working Papers in Economics 2448, Faculty of Economics, University of Cambridge.
• Karch, Julian D. & Perez-Alonso, Andres F. & Bergsma, Wicher P., 2024. "Beyond Pearson’s correlation: modern nonparametric independence tests for psychological research," LSE Research Online Documents on Economics 124587, London School of Economics and Political Science, LSE Library.
• Zhang, Junyi & Dassios, Angelos, 2024. "Posterior sampling from truncated Ferguson-Klass representation of normalised completely random measure mixtures," LSE Research Online Documents on Economics 122228, London School of Economics and Political Science, LSE Library.
• Kerwin, Jason & Rostom, Nada & Sterck, Olivier, 2024. "Striking the Right Balance: Why Standard Balance Tests Over-Reject the Null, and How to Fix It," IZA Discussion Papers 17217, Institute of Labor Economics (IZA).
• Adam Dearing & Lorenzo Magnolfi & Daniel Quint & Christopher J. Sullivan & Sarah B. Waldfogel, 2024. "Learning Firm Conduct: Pass-Through as a Foundation for Instrument Relevance," NBER Working Papers 32863, National Bureau of Economic Research, Inc.
• Meyer-Gohde, Alexander, 2024. "Solving and analyzing DSGE models in the frequency domain," IMFS Working Paper Series 207, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
• Zitian Gao & Yihao Xiao, 2024. "Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method," Papers 2408.09420, arXiv.org, revised Dec 2024.
• Parvin Malekzadeh & Zissis Poulos & Jacky Chen & Zeyu Wang & Konstantinos N. Plataniotis, 2024. "EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning," Papers 2408.12446, arXiv.org, revised Aug 2024.
• Soren Bettels & Stefan Weber, 2024. "An Integrated Approach to Importance Sampling and Machine Learning for Efficient Monte Carlo Estimation of Distortion Risk Measures in Black Box Models," Papers 2408.02401, arXiv.org, revised Jan 2025.
• Cameron Cornell & Lewis Mitchell & Matthew Roughan, 2024. "Enhancing Causal Discovery in Financial Networks with Piecewise Quantile Regression," Papers 2408.12210, arXiv.org.