Report NEP-ECM-2021-04-12

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:

• Jonathan Roth & Pedro H. C. Sant'Anna, 2021, "Efficient Estimation for Staggered Rollout Designs," Papers, arXiv.org, number 2102.01291, Feb, revised May 2023.
• Alain Hecq & Li Sun, 2021, "Adaptive Random Bandwidth for Inference in CAViaR Models," Papers, arXiv.org, number 2102.01636, Feb.
• Woraphon Yamaka & Rangan Gupta & Sukrit Thongkairat & Paravee Maneejuk, 2021, "Structural and Predictive Analyses with a Mixed Copula-Based Vector Autoregression Model," Working Papers, University of Pretoria, Department of Economics, number 202108, Jan.
• Acerenza, Santiago & Bartalotti, Otávio & Kedagni, Desire, 2021, "Testing Identifying Assumptions in Bivariate Probit Models," ISU General Staff Papers, Iowa State University, Department of Economics, number 202103290700001124, Mar.
• Federico A. Bugni & Mengsi Gao, 2021, "Inference under Covariate-Adaptive Randomization with Imperfect Compliance," Papers, arXiv.org, number 2102.03937, Feb, revised Jul 2023.
• Giuseppe Cavaliere & Ye Lu & Anders Rahbek & Jacob St{ae}rk-{O}stergaard, 2021, "Bootstrap Inference for Hawkes and General Point Processes," Papers, arXiv.org, number 2104.03122, Apr, revised Sep 2021.
• Item repec:luc:wpaper:21-07 is not listed on IDEAS anymore

• Alfelt, Gustav & Bodnar, Taras & Javed, Farrukh & Tyrcha, Joanna, 2020, "Singular conditional autoregressive Wishart model for realized covariance matrices," Working Papers, Örebro University, School of Business, number 2021:1, Oct.
• Emanuele Bacchiocchi & Toru Kitagawa, 2021, "On global identification in structural vector autoregressions," Papers, arXiv.org, number 2102.04048, Feb, revised Mar 2026.
• Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Minchul Shin, 2021, "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers, FEDEA, number 2021-09, Mar.
• Virk, Nader & Javed, Farrukh & Awartani, Basel, 2021, "A reality check on the GARCH-MIDAS volatility models," Working Papers, Örebro University, School of Business, number 2021:2, Mar.
• AmirEmad Ghassami & Andrew Ying & Ilya Shpitser & Eric Tchetgen Tchetgen, 2021, "Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference," Papers, arXiv.org, number 2104.02929, Apr, revised Mar 2022.
• Eduardo Garcia-Portugues & Davy Paindaveine & Thomas Verdebout, 2020, "On optimal tests for rotational symmetry against new classes of hyperspherical distributions," Post-Print, HAL, number hal-03169388, Sep, DOI: 10.1080/01621459.2019.1665527.
• Jeppe Druedahl & Michael Graber & Thomas H. J�rgensen, 2021, "High Frequency Income Dynamics," CEBI working paper series, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI), number 21-08, Mar.
• Yuchen Hu & Shuangning Li & Stefan Wager, 2021, "Average Direct and Indirect Causal Effects under Interference," Papers, arXiv.org, number 2104.03802, Apr, revised Jan 2022.
• Fengler, Matthias & Polivka, Jeannine, 2021, "Proxy-identification of a structural MGARCH model for asset returns," Economics Working Paper Series, University of St. Gallen, School of Economics and Political Science, number 2103, Apr, revised Oct 2024.
• YingHua He & Shruti Sinha & Xiaoting Sun, 2021, "Identification and Estimation in Many-to-one Two-sided Matching without Transfers," Papers, arXiv.org, number 2104.02009, Apr, revised Jul 2023.
• Fiorini, Mario & Stevens, Katrien, 2021, "Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs," Working Papers, University of Sydney, School of Economics, number 2021-01, Feb.
• Jonathan Berrisch & Florian Ziel, 2021, "CRPS Learning," Papers, arXiv.org, number 2102.00968, Feb, revised Nov 2021.
• Marcelo C. Medeiros & Henrique F. Pires, 2021, "The Proper Use of Google Trends in Forecasting Models," Papers, arXiv.org, number 2104.03065, Apr, revised Apr 2021.
• Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2019, "Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations," Papers, arXiv.org, number 1912.09002, Dec, revised Jun 2021.
• Marisa Miraldo & Carol Propper & Christiern Rose, 2020, "Identification of Peer Effects using Panel Data," Discussion Papers Series, School of Economics, University of Queensland, Australia, number 639, Dec.
• Jacob Dorn & Kevin Guo, 2021, "Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing," Papers, arXiv.org, number 2102.04543, Feb, revised Aug 2023.
• Milad Haghani & Michiel C. J. Bliemer & John M. Rose & Harmen Oppewal & Emily Lancsar, 2021, "Hypothetical bias in stated choice experiments: Part II. Macro-scale analysis of literature and effectiveness of bias mitigation methods," Papers, arXiv.org, number 2102.02945, Feb.
• James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2022, "Cluster-Robust Inference: A Guide to Empirical Practice," Working Paper, Economics Department, Queen's University, number 1456, Mar.
• Andrea Vandin & Daniele Giachini & Francesco Lamperti & Francesca Chiaromonte, 2021, "Automated and Distributed Statistical Analysis of Economic Agent-Based Models," Papers, arXiv.org, number 2102.05405, Feb, revised Nov 2023.
• Mathur, Maya B & Smith, Louisa & Yoshida, Kazuki & Ding, Peng & VanderWeele, Tyler, 2021, "E-values for effect heterogeneity and conservative approximations for causal interaction," OSF Preprints, Center for Open Science, number h6pru, Apr, DOI: 10.31219/osf.io/h6pru.
• Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2021, "A first-stage representation for instrumental variables quantile regression," Papers, arXiv.org, number 2102.01212, Feb, revised Feb 2022.
• Julian Ramajo & Miguel A. Marquez & Geoffrey J. D. Hewings, 2021, "Addressing spatial dependence in technical efficiency estimation: A Spatial DEA frontier approach," Papers, arXiv.org, number 2103.14063, Mar.
• Pratyush Muthukumar & Jie Zhong, 2021, "A Stochastic Time Series Model for Predicting Financial Trends using NLP," Papers, arXiv.org, number 2102.01290, Feb.
• Filippo Neri, 2021, "Domain Specific Concept Drift Detectors for Predicting Financial Time Series," Papers, arXiv.org, number 2103.14079, Mar, revised Sep 2021.

Report NEP-ECM-2021-04-12

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: