Report NEP-ECM-2022-02-21

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:

• Jinyong Hahn & Hyungsik Roger Moon & Ruoyao Shi, 2022. "Test of Neglected Heterogeneity in Dyadic Models," Working Papers 202206, University of California at Riverside, Department of Economics.
• Kenichi Shimizu, 2022. "Asymptotic properties of Bayesian inference in linear regression with a structural break," Working Papers 2022_05, Business School - Economics, University of Glasgow.
• Andriy Norets & Kenichi Shimizu, 2022. "Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models," Working Papers 2022_06, Business School - Economics, University of Glasgow.
• Ke-Li Xu & Junjie Guo, 2021. "A New Test for Multiple Predictive Regression," CAEPR Working Papers 2022-001 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
• Chen, Yunxiao & Li, Xiaoou, 2022. "Determining the number of factors in high-dimensional generalized latent factor models," LSE Research Online Documents on Economics 111574, London School of Economics and Political Science, LSE Library.
• Ron Mittelhammer & George Judge & Miguel Henry, 2022. "An Entropy-Based Approach for Nonparametrically Testing Simple Probability Distribution Hypotheses," Papers 2201.06647, arXiv.org.
• Debasis Kundu, 2021. "Stationary GE-Process and its Application in Analyzing Gold Price Data," Papers 2201.02568, arXiv.org.
• Xiaohong Chen & Zhengling Qi, 2022. "On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation," Papers 2201.06169, arXiv.org, revised Jun 2022.
• Ando, Tomohiro & Bai, Jushan, 2021. "Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity," MPRA Paper 111431, University Library of Munich, Germany.
• Jurkatis, Simon, 2022. "Why you should not use the LSV herding measure," Bank of England working papers 959, Bank of England.
• George Planiteros, 2022. "Reverse matching for ex-ante policy evaluation," DEOS Working Papers 2206, Athens University of Economics and Business.
• Nathan Kallus, 2022. "Treatment Effect Risk: Bounds and Inference," Papers 2201.05893, arXiv.org, revised Jul 2022.
• Vembye, Mikkel Helding & Pustejovsky, James E & Pigott, Terri, 2022. "Power Approximations for Meta-Analysis of Dependent Effect Sizes," MetaArXiv 6tp9y, Center for Open Science.
• Li, Xiaoou & Chen, Yunxiao & Chen, Xi & Liu, Jingchen & Ying, Zhiliang, 2021. "Optimal stopping and worker selection in crowdsourcing: an adaptive sequential probability ratio test framework," LSE Research Online Documents on Economics 100873, London School of Economics and Political Science, LSE Library.
• Kristoffer Pons Bertelsen, 2022. "The Prior Adaptive Group Lasso and the Factor Zoo," CREATES Research Papers 2022-05, Department of Economics and Business Economics, Aarhus University.
• Joshi, Megha & Pustejovsky, James E & Beretvas, S. Natasha, 2021. "Cluster Wild Bootstrapping to Handle Dependent Effect Sizes in Meta-Analysis with a Small Number of Studies," MetaArXiv x6uhk, Center for Open Science.
• Yuen, Christine & Fryzlewicz, Piotr, 2022. "Exploiting disagreement between high-dimensional variable selectors for uncertainty visualization," LSE Research Online Documents on Economics 112480, London School of Economics and Political Science, LSE Library.
• Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2022. "Bayesian Estimation of Multivariate Panel Probits with Higher-order Network Interdependence and an Application to Firms' Global Market Participation in Guangdong," Center for Policy Research Working Papers 247, Center for Policy Research, Maxwell School, Syracuse University.
• Margherita Fort & Andrea Ichino & Enrico Rettore & Giulio Zanella, 2022. "Multi-cutoff RD designs with observations located at each cutoff: problems and solutions," "Marco Fanno" Working Papers 0278, Dipartimento di Scienze Economiche "Marco Fanno".
• Thomas R. Cook & Greg Gupton & Zach Modig & Nathan M. Palmer, 2021. "Explaining Machine Learning by Bootstrapping Partial Dependence Functions and Shapley Values," Research Working Paper RWP 21-12, Federal Reserve Bank of Kansas City.
• Chen, Yudong & Wang, Tengyao & Samworth, Richard J., 2022. "High-dimensional, multiscale online changepoint detection," LSE Research Online Documents on Economics 113665, London School of Economics and Political Science, LSE Library.
• Yasumasa Matsuda & Xin Yuan, 2022. "Convolutional regression for big spatial data," DSSR Discussion Papers 124, Graduate School of Economics and Management, Tohoku University.
• Braun, Robin, 2021. "The importance of supply and demand for oil prices: evidence from non-Gaussianity," Bank of England working papers 957, Bank of England.
• Banker, Rajiv & Park, Han-Up & Sahoo, Biresh, 2022. "A statistical foundation for the measurement of managerial ability," MPRA Paper 111832, University Library of Munich, Germany.
• Luke De Clerk & Sergey Savl'ev, 2022. "A machine learning search for optimal GARCH parameters," Papers 2201.03286, arXiv.org.