Report NEP-ECM-2022-08-15

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:

• Tom Boot & Gianmaria Niccodemi & Tom Wansbeek, 2022. "Unbiased estimation of the OLS covariance matrix when the errors are clustered," Papers 2206.09644, arXiv.org.
• Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling," Papers 2206.14275, arXiv.org, revised Feb 2024.
• Simone Tonini & Francesca Chiaromonte & Alessandro Giovannelli, 2022. "On the impact of serial dependence on penalized regression methods," LEM Papers Series 2022/21, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
• Taisuke Otsu & Mengshan Xu, 2022. "Isotonic propensity score matching," STICERD - Econometrics Paper Series 623, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
• Dimitris Korobilis, 2022. "A new algorithm for structural restrictions in Bayesian vector autoregressions," Papers 2206.06892, arXiv.org.
• Li, Kunpeng, 2022. "Threshold spatial autoregressive model," MPRA Paper 113568, University Library of Munich, Germany.
• Chen, Zezhun & Dassios, Angelos & Tzougas, George, 2022. "Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression," LSE Research Online Documents on Economics 115369, London School of Economics and Political Science, LSE Library.
• Helton Saulo & Roberto Vila & Shayane S. Cordeiro, 2022. "Symmetric generalized Heckman models," Papers 2206.10054, arXiv.org.
• Jinyong Hahn & David W. Hughes & Guido Kuersteiner & Whitney K. Newey, 2022. "Efficient Bias Correction for Cross-section and Panel Data," Papers 2207.09943, arXiv.org, revised Jan 2024.
• Christian Bongiorno & Damien Challet, 2022. "Statistical inference of lead-lag at various timescales between asynchronous time series from p-values of transfer entropy," Papers 2206.10173, arXiv.org.
• Joshua Chan & Eric Eisenstat & Xuewen Yu, 2022. "Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis," Papers 2207.03988, arXiv.org.
• Chang, Jinyuan & Cheng, Guanghui & Yao, Qiwei, 2022. "Testing for unit roots based on sample autocovariances," LSE Research Online Documents on Economics 114620, London School of Economics and Political Science, LSE Library.
• Weronika Ormaniec & Marcin Pitera & Sajad Safarveisi & Thorsten Schmidt, 2022. "Estimating value at risk: LSTM vs. GARCH," Papers 2207.10539, arXiv.org.
• Mario P. Rothfelder & Otilia Boldea, 2022. "Testing for a Threshold in Models with Endogenous Regressors," Papers 2207.10076, arXiv.org.
• Danyu Lin, 2022. "Fitting the Cox proportional hazards model to interval-censored data," Biostatistics and Epidemiology Virtual Symposium 2022 04, Stata Users Group.
• Oliver R. Cutbill & Rami V. Tabri, 2022. "The Impossibility of Testing for Dependence Using Kendall’s Ƭ Under Missing Data of Unknown Form," Working Papers 2022-03, University of Sydney, School of Economics.
• Yan Liu, 2022. "Policy Learning under Endogeneity Using Instrumental Variables," Papers 2206.09883, arXiv.org, revised Mar 2024.
• Chiranjit Dutta & Nalini Ravishanker & Sumanta Basu, 2022. "Modeling Multivariate Positive-Valued Time Series Using R-INLA," Papers 2206.05374, arXiv.org, revised Jul 2022.
• Timothy G. Conley & Bill Dupor & Mahdi Ebsim, 2022. "The Sine Aggregatio Approach to Applied Macro," Working Papers 2022-014, Federal Reserve Bank of St. Louis, revised 11 Nov 2022.
• Item repec:cte:wsrepe:35465 is not listed on IDEAS anymore

• Collin Philipps, 2022. "An Expectile Strong Law of Large Numbers," Working Papers 2022-05, Department of Economics and Geosciences, US Air Force Academy.
• Federico Bassetti & Roberto Casarin & Marco Del Negro, 2022. "A Bayesian Approach to Inference on Probabilistic Surveys," Staff Reports 1025, Federal Reserve Bank of New York.
• Di Zhang & Qiang Niu & Youzhou Zhou, 2022. "Modeling Randomly Walking Volatility with Chained Gamma Distributions," Papers 2207.01151, arXiv.org, revised Oct 2022.
• Anthony Coache & Sebastian Jaimungal & 'Alvaro Cartea, 2022. "Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning," Papers 2206.14666, arXiv.org, revised May 2023.
• Andrew Y. Chen, 2022. "Most claimed statistical findings in cross-sectional return predictability are likely true," Papers 2206.15365, arXiv.org, revised Sep 2024.
• Bryan T. Kelly & Semyon Malamud & Kangying Zhou, 2022. "The Virtue of Complexity in Return Prediction," NBER Working Papers 30217, National Bureau of Economic Research, Inc.