Report NEP-ECM-2018-05-21

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.

Subscribe to this report: email, RSS, or Mastodon.

Other reports in NEP-ECM

The following items were announced in this report:

• Gabriele Fiorentini & Enrique Sentana, 2018. "Specification tests for non-Gaussian maximum likelihood estimators," Working Paper series 18-22, Rimini Centre for Economic Analysis.
• Mika Meitz & Daniel Preve & Pentti Saikkonen, 2018. "A mixture autoregressive model based on Student's $t$-distribution," Papers 1805.04010, arXiv.org.
• Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Papers 1805.03807, arXiv.org.
• Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich towards a “General Generalised Distance”," Working Papers ECARES 2018-12, ULB -- Universite Libre de Bruxelles.
• Breitung, Jörg & Knüppel, Malte, 2018. "How far can we forecast? Statistical tests of the predictive content," Discussion Papers 07/2018, Deutsche Bundesbank.
• Li, Yong & Liu, Xiaobin & Zeng, Tao & Yu, Jun, 2018. "A Posterior-Based Wald-Type Statistic for Hypothesis Testing," Economics and Statistics Working Papers 8-2018, Singapore Management University, School of Economics.
• Juan Carlos Escanciano & Wei Li, 2018. "Optimal Linear Instrumental Variables Approximations," Papers 1805.03275, arXiv.org, revised Feb 2020.
• Schreiber, Sven, 2018. "Are bootstrapped cointegration test findings unreliable?," Discussion Papers 2018/8, Free University Berlin, School of Business & Economics.
• Takaki Sato & Yasumasa Matsuda, 2018. "Spatiotemporal ARCH Models," DSSR Discussion Papers 82, Graduate School of Economics and Management, Tohoku University.
• Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2018. "A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors," MPRA Paper 86371, University Library of Munich, Germany.
• Tyler Atkinson & Alexander W. Richter & Nathaniel A. Throckmorton, 2018. "The Zero Lower Bound and Estimation Accuracy," Working Papers 1804, Federal Reserve Bank of Dallas.
• Atkinson, Anthony C. & Riani, Marco & Cerioli, Andrea, 2017. "Cluster detection and clustering with random start forward searches," LSE Research Online Documents on Economics 72291, London School of Economics and Political Science, LSE Library.
• Simon Freyaldenhoven & Christian Hansen & Jesse M. Shapiro, 2018. "Pre-event Trends in the Panel Event-study Design," NBER Working Papers 24565, National Bureau of Economic Research, Inc.
• Victor De Oliveira, 2017. "Geostatistical Binary Data: Models, Properties And Connections," Working Papers 0151mss, College of Business, University of Texas at San Antonio.
• VÁZQUEZ-ALCOCER, Alan & SCHOEN, Eric D. & GOOS, Peter, 2018. "A mixed integer optimization approach for model selection in screening experiments," Working Papers 2018007, University of Antwerp, Faculty of Business and Economics.
• Soeren Johansen, 2018. "Cointegration and adjustment in the infinite order CVAR representation of some partially observed CVAR(1) models," Discussion Papers 18-05, University of Copenhagen. Department of Economics.
• Alexis Akira Toda, 2018. "Data-based Automatic Discretization of Nonparametric Distributions," Papers 1805.00896, arXiv.org, revised May 2019.
• Lisa Schlosser & Torsten Hothorn & Reto Stauffer & Achim Zeileis, 2018. "Distributional regression forests for probabilistic precipitation forecasting in complex terrain," Working Papers 2018-08, Faculty of Economics and Statistics, Universität Innsbruck, revised Nov 2018.
• Jeffrey S. Racine & Qi Li & Li Zheng, 2018. "Optimal Model Averaging of Mixed-Data Kernel-Weighted Spline Regressions," Department of Economics Working Papers 2018-10, McMaster University.
• Arkadiusz Koziol & Anuradha Roy & Roman Zmyslony & Ricardo Leiva & Miguel Fonseca, 2016. "Best unbiased estimates for parameters of three-level multivariate data with doubly exchangeable covariance structure," Working Papers 0149mss, College of Business, University of Texas at San Antonio.
• Shige Peng & Shuzhen Yang & Jianfeng Yao, 2018. "Improving Value-at-Risk prediction under model uncertainty," Papers 1805.03890, arXiv.org, revised Jun 2020.
• Myasnikov, Alexander, 2018. "Оценка Пространственных Моделей С Переменными Коэффициентами Пространственной Чувствительности Методом Максимального Правдоподобия И Обобщенным Методом Наименьших Квадратов [Maximum likelihood and ," MPRA Paper 86696, University Library of Munich, Germany.