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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.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-ECM
The following items were announced in this report:
- Ying Zeng, 2024, "Estimation and Inference on Average Treatment Effect in Percentage Points under Heterogeneity," Papers, arXiv.org, number 2408.06624, Aug, revised Feb 2026.
- Stephan Hetzenecker & Maximilian Osterhaus, 2024, "Deep Learning for the Estimation of Heterogeneous Parameters in Discrete Choice Models," Papers, arXiv.org, number 2408.09560, Aug.
- Wenjie Wang & Yichong Zhang, 2024, "Gradient Wild Bootstrap for Instrumental Variable Quantile Regressions with Weak and Few Clusters," Papers, arXiv.org, number 2408.10686, Aug.
- Alexander Mayer & Dominik Wied, 2024, "Endogeneity Corrections in Binary Outcome Models with Nonlinear Transformations: Identification and Inference," Papers, arXiv.org, number 2408.06977, Aug, revised May 2025.
- Cong Wang, 2024, "Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis," Papers, arXiv.org, number 2408.09271, Aug, revised Sep 2024.
- Abhinandan Dalal & Patrick Blobaum & Shiva Kasiviswanathan & Aaditya Ramdas, 2024, "Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters," Papers, arXiv.org, number 2408.09598, Aug, revised Sep 2024.
- Lucas Z. Zhang, 2024, "Continuous difference-in-differences with double/debiased machine learning," Papers, arXiv.org, number 2408.10509, Aug, revised Dec 2025.
- Guanghui Pan, 2024, "Methodological Foundations of Modern Causal Inference in Social Science Research," Papers, arXiv.org, number 2408.00032, Jul.
- Bhattacharjee, A. & Ditzen, J. & Holly, S., 2024, "Engle-Granger Representation in Spatial and Spatio-Temporal Models," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2447, Aug.
- Harvey, A. & Simons, J., 2024, "Hidden Threshold Models with applications to asymmetric cycles," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2448, Aug.
- 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, London School of Economics and Political Science, LSE Library, number 124587, Sep.
- Zhang, Junyi & Dassios, Angelos, 2025, "Posterior sampling from truncated Ferguson-Klass representation of normalised completely random measure mixtures," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 122228, Jan.
- 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, IZA Network @ LISER, number 17217, Aug.
- 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, National Bureau of Economic Research, Inc, number 32863, Aug.
- Meyer-Gohde, Alexander, 2024, "Solving and analyzing DSGE models in the frequency domain," IMFS Working Paper Series, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), number 207.
- Zitian Gao & Yihao Xiao, 2024, "Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method," Papers, arXiv.org, number 2408.09420, Aug, revised Mar 2025.
- Parvin Malekzadeh & Zissis Poulos & Jacky Chen & Zeyu Wang & Konstantinos N. Plataniotis, 2024, "EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning," Papers, arXiv.org, number 2408.12446, Aug, 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, arXiv.org, number 2408.02401, Aug, revised Aug 2025.
- Cameron Cornell & Lewis Mitchell & Matthew Roughan, 2024, "Enhancing Causal Discovery in Financial Networks with Piecewise Quantile Regression," Papers, arXiv.org, number 2408.12210, Aug.