Report NEP-ECM-2022-12-19

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:

• Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2022, "Bayesian Neural Networks for Macroeconomic Analysis," Papers, arXiv.org, number 2211.04752, Nov, revised Apr 2024.
• Eoghan O'Neill, 2022, "Type I Tobit Bayesian Additive Regression Trees for Censored Outcome Regression," Papers, arXiv.org, number 2211.07506, Nov, revised Feb 2024.
• Clint Harris, 2022, "Interpreting Instrumental Variable Estimands with Unobserved Treatment Heterogeneity: The Effects of College Education," Papers, arXiv.org, number 2211.13132, Nov.
• Michael P. Leung & Pantelis Loupos, 2022, "Graph Neural Networks for Causal Inference Under Network Confounding," Papers, arXiv.org, number 2211.07823, Nov, revised Dec 2025.
• Giovanni Mellace & Alessandra Pasquini, 2022, "Mediation Analysis Synthetic Control," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1389, Nov.
• Peter Arcidiacono & Attila Gyetvai & Arnaud Maurel & Ekaterina S. Jardim, 2022, "Identification and Estimation of Continuous-Time Job Search Models with Preference Shocks," NBER Working Papers, National Bureau of Economic Research, Inc, number 30655, Nov.
• Nguyen, Loc PhD, PostDoc, 2022, "Extreme bound analysis based on correlation coefficient for optimal regression model," OSF Preprints, Center for Open Science, number wjnz3, Nov, DOI: 10.31219/osf.io/wjnz3.
• Khushboo Surana, 2022, "How different are we? Identifying the degree of revealed preference heterogeneity," Discussion Papers, Department of Economics, University of York, number 22/09, Nov.
• Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022, "Constructing Fan Charts from the Ragged Edge of SPF Forecasts," Working Papers, Federal Reserve Bank of Cleveland, number 22-36, Nov, DOI: 10.26509/frbc-wp-202236.
• Steven Campbell & Ting-Kam Leonard Wong, 2022, "Efficient convex PCA with applications to Wasserstein GPCA and ranked data," Papers, arXiv.org, number 2211.02990, Nov, revised Aug 2024.
• Katarzyna Kopczewska, 2021, "Spatial Machine Learning – New Opportunities for Regional Science," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-16.
• Duo Qin, 2022, "Redirect the Probability Approach in Econometrics Towards PAC Learning," Working Papers, Department of Economics, SOAS University of London, UK, number 249, Mar.