Report NEP-ECM-2021-06-14

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:

• Liang Jiang & Xiaobin Liu & Peter C.B. Phillips & Yichong Zhang, 2021. "Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations," Cowles Foundation Discussion Papers 2288, Cowles Foundation for Research in Economics, Yale University.
• Matei Demetrescu & Robinson Kruse-Becher, 2021. "Is U.S. real output growth really non-normal? Testing distributional assumptions in time-varying location-scale models," CREATES Research Papers 2021-07, Department of Economics and Business Economics, Aarhus University.
• Yong Cai, 2021. "Panel Data with Unknown Clusters," Papers 2106.05503, arXiv.org, revised Jan 2022.
• Yuanhua Feng & Wolfgang Karl Härdle, 2021. "Uni- and multivariate extensions of the sinh-arcsinh normal distribution applied to distributional regression," Working Papers CIE 142, Paderborn University, CIE Center for International Economics.
• Martin Garcia-Vazquez, 2021. "Identification and Estimation of Non-stationary Hidden Markov Models," Working Papers 2021-023, Human Capital and Economic Opportunity Working Group.
• Kohtaro Hitomi & Keiji Nagai & Yoshihiko Nishiyama & Junfan Tao, 2021. "Joint Asymptotic Properties of Stopping Times and Sequential Estimators for Stationary First-order Autoregressive Models," KIER Working Papers 1060, Kyoto University, Institute of Economic Research.
• Griffin, Jim E. & Mitrodima, Gelly, 2020. "A Bayesian quantile time series model for asset returns," LSE Research Online Documents on Economics 105610, London School of Economics and Political Science, LSE Library.
• John List & Azeem Shaikh & Atom Vayalinkal, 2023. "Multiple Testing with Covariate Adjustment in Experimental Economics," Natural Field Experiments 00732, The Field Experiments Website.
• Blankmeyer, Eric, 2021. "Explorations in NISE Estimation," MPRA Paper 108179, University Library of Munich, Germany.
• Jonas E. Arias & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Macroeconomic Forecasting and Variable Ordering in Multivariate Stochastic Volatility Models," Working Papers 21-21, Federal Reserve Bank of Philadelphia.
• David M. Kaplan & Xin Liu, 2021. "k-Class Instrumental Variables Quantile Regression," Working Papers 2104, Department of Economics, University of Missouri.
• Mateusz Buczyński & Marcin Chlebus, 2021. "GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks," Working Papers 2021-08, Faculty of Economic Sciences, University of Warsaw.
• James Mitchell & Martin Weale, 2021. "Censored Density Forecasts: Production and Evaluation," Working Papers 21-12R, Federal Reserve Bank of Cleveland, revised 16 Aug 2022.
• Jin Seo Cho & Matthew Greenwood-Nimmo & Yongcheol Shin, 2021. "Recent Developments of the Autoregressive Distributed Lag Modelling Framework," Working papers 2021rwp-186, Yonsei University, Yonsei Economics Research Institute.
• Johann Pfitzinger, 2021. "An Interpretable Neural Network for Parameter Inference," Papers 2106.05536, arXiv.org.
• Minsu Chang & Xiaohong Chen & Frank Schorfheide, 2021. "Heterogeneity and Aggregate Fluctuations," Cowles Foundation Discussion Papers 2289, Cowles Foundation for Research in Economics, Yale University.
• Laura Coroneo & Fabrizio Iacone, 2021. "Testing for equal predictive accuracy with strong dependence," Discussion Papers 21/03, Department of Economics, University of York.
• Aguirregabiria, Victor, 2020. "Identification of Firms' Beliefs in Structural Models of Market Competition," CEPR Discussion Papers 14975, C.E.P.R. Discussion Papers.
• Paul Glasserman & Mike Li, 2021. "Linear Classifiers Under Infinite Imbalance," Papers 2106.05797, arXiv.org, revised May 2023.
• Bobeica, Elena & Hartwig, Benny, 2021. "The COVID-19 shock and challenges for time series models," Working Paper Series 2558, European Central Bank.
• Michel Lubrano & Zhou Xun, 2021. "The Bayesian approach to poverty measurement," AMSE Working Papers 2133, Aix-Marseille School of Economics, France.