Report NEP-ECM-2020-05-11

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:

• Zongwu Cai & Ying Fang & Qiuhua Xu, 2020. "Testing Capital Asset Pricing Models using Functional-Coefficient Panel Data Models with Cross-Sectional Dependence," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202009, University of Kansas, Department of Economics, revised Jul 2020.
• Eric Hillebrand & Manuel Lukas & Wei Wei, 2020. "Bagging Weak Predictors," Monash Econometrics and Business Statistics Working Papers 16/20, Monash University, Department of Econometrics and Business Statistics.
• Kim, Jihyun & Meddahi, Nour, 2020. "Volatility Regressions with Fat Tails," TSE Working Papers 20-1097, Toulouse School of Economics (TSE).
• Svetlana Litvinova & Mervyn J. Silvapulle, 2020. "Consistency of full-sample bootstrap for estimating high-quantile, tail probability, and tail index," Monash Econometrics and Business Statistics Working Papers 15/20, Monash University, Department of Econometrics and Business Statistics.
• Piero Mazzarisi & Silvia Zaoli & Carlo Campajola & Fabrizio Lillo, 2020. "Tail Granger causalities and where to find them: extreme risk spillovers vs. spurious linkages," Papers 2005.01160, arXiv.org, revised May 2021.
• Poncela, Pilar & Ruiz, Esther, 2020. "A comment on the dynamic factor model with dynamic factors," Economics Discussion Papers 2020-7, Kiel Institute for the World Economy (IfW Kiel).
• Masayuki Sawada & Kohei Kawaguchi, 2020. "Estimating High-Dimensional Discrete Choice Model of Differentiated Products with Random Coefficients," Papers 2004.08791, arXiv.org.
• Chen, J.; & Gu, Y.; & Jones, A.M.; & Peng, B.;, 2020. "Modelling healthcare costs: a semiparametric extension of generalised linear models," Health, Econometrics and Data Group (HEDG) Working Papers 20/03, HEDG, c/o Department of Economics, University of York.
• Dimitris Korobilis & Davide Pettenuzzo, 2020. "Machine Learning Econometrics: Bayesian algorithms and methods," Papers 2004.11486, arXiv.org.
• Karlsson, Sune & Mazur, Stepan, 2020. "Flexible Fat-tailed Vector Autoregression," Working Papers 2020:5, Örebro University, School of Business.
• Yeonwoo Rho & Yun Liu & Hie Joo Ahn, 2020. "Revealing Cluster Structures Based on Mixed Sampling Frequencies," Papers 2004.09770, arXiv.org, revised Feb 2021.
• Mengya Liu & Fukan Zhu & Ke Zhu, 2020. "Multi-frequency-band tests for white noise under heteroskedasticity," Papers 2004.09161, arXiv.org.
• Giuseppe Cavaliere & Heino Bohn Nielsen & Anders Rahbek, 2020. "An Introduction To Bootstrap Theory In Time Series Econometrics," Discussion Papers 20-02, University of Copenhagen. Department of Economics.
• Andreas Tryphonides, 2020. "Identifying Preferences when Households are Financially Constrained," Papers 2005.02010, arXiv.org, revised Feb 2023.
• Kim, Jihyun & Park, Joon & Wang, Bin, 2020. "Estimation of Volatility Functions in Jump Diffusions Using Truncated Bipower Increments," TSE Working Papers 20-1096, Toulouse School of Economics (TSE).
• Jean-Jacques Forneron & Serena Ng, 2020. "Inference by Stochastic Optimization: A Free-Lunch Bootstrap," Papers 2004.09627, arXiv.org, revised Sep 2020.
• Thomas-Agnan, Christine & Laurent, Thibault & Ruiz-Gazen, Anne & Nguyen, T.H.A & Chakir, Raja & Lungarska, Anna, 2020. "Spatial simultaneous autoregressive models for compositional data: Application to land use," TSE Working Papers 20-1098, Toulouse School of Economics (TSE).
• Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
• Zolotareva, Anna (Золотарева, Анна) & Kireeva, Anastasia (Киреева, Анастасия), 2020. "Prospects and options for codification of payments levied in order to compensate for harm (damage) to the environment [Перспективы И Варианты Кодификации Платежей, Взимаемых В Целях Возмещения Вред," Working Papers 032015, Russian Presidential Academy of National Economy and Public Administration.
• Ursula Laa & Dianne Cook & Andreas Buja & German Valencia, 2020. "Hole or grain? A Section Pursuit Index for Finding Hidden Structure in Multiple Dimensions," Monash Econometrics and Business Statistics Working Papers 17/20, Monash University, Department of Econometrics and Business Statistics.
• Ruda Zhang & Patrick Wingo & Rodrigo Duran & Kelly Rose & Jennifer Bauer & Roger Ghanem, 2020. "Environmental Economics and Uncertainty: Review and a Machine Learning Outlook," Papers 2004.11780, arXiv.org.
• Chan, Mark K. & Kwok, Simon, 2020. "The PCDID Approach: Difference-in-Differences when Trends are Potentially Unparallel and Stochastic," Working Papers 2020-03, University of Sydney, School of Economics.
• Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
• Michael Roberts & Indranil SenGupta, 2020. "Sequential hypothesis testing in machine learning, and crude oil price jump size detection," Papers 2004.08889, arXiv.org, revised Dec 2020.
• Mattia Guerini & Patrick Musso & Lionel Nesta, 2020. "Estimation of Threshold Distributions for Market Participation," LEM Papers Series 2020/09, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
• Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.
• Arjun Prakash & Nick James & Max Menzies & Gilad Francis, 2020. "Structural clustering of volatility regimes for dynamic trading strategies," Papers 2004.09963, arXiv.org, revised Nov 2021.
• Jesper R.-V. Soerensen & Mogens Fosgerau, 2020. "How McFadden met Rockafellar and learnt to do more with less," Discussion Papers 20-01, University of Copenhagen. Department of Economics.
• Alexander Arimond & Damian Borth & Andreas Hoepner & Michael Klawunn & Stefan Weisheit, 2020. "Neural Networks and Value at Risk," Papers 2005.01686, arXiv.org, revised May 2020.