Report NEP-ETS-2020-08-31

This is the archive for NEP-ETS, a report on new working papers in the area of Econometric Time Series. Jaqueson K. Galimberti issued this report. It is usually issued weekly.

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

Other reports in NEP-ETS

The following items were announced in this report:

• Peter C.B. Phillips & Ying Wang, 2020. "When Bias Contributes to Variance: True Limit Theory in Functional Coefficient Cointegrating Regression," Cowles Foundation Discussion Papers 2250, Cowles Foundation for Research in Economics, Yale University.
• Kwok, Simon, 2020. "Nonparametric Inference of Jump Autocorrelation," Working Papers 2020-09, University of Sydney, School of Economics, revised Jan 2021.
• Ke Miao & Peter C.B. Phillips & Liangjun Su, 2020. "High-Dimensional VARs with Common Factors," Cowles Foundation Discussion Papers 2252, Cowles Foundation for Research in Economics, Yale University.
• Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," MPRA Paper 102315, University Library of Munich, Germany.
• Ruijun Bu & Jihyun Kim & Bin Wang, 2020. "Uniform and Lp Convergences of Nonparametric Estimation for Diffusion Models," Working Papers 202021, University of Liverpool, Department of Economics.
• Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2020. "Nowcasting with large Bayesian vector autoregressions," Working Paper Series 2453, European Central Bank.
• Ye Chen & Peter C.B. Phillips & Shuping Shi, 2020. "Common Bubble Detection in Large Dimensional Financial Systems," Cowles Foundation Discussion Papers 2251, Cowles Foundation for Research in Economics, Yale University.
• Jos'e Luis Montiel Olea & Mikkel Plagborg-M{o}ller, 2020. "Local Projection Inference is Simpler and More Robust Than You Think," Papers 2007.13888, arXiv.org, revised Dec 2022.
• Federico A. Bugni & Jia Li & Qiyuan Li, 2020. "Permutation-based tests for discontinuities in event studies," Papers 2007.09837, arXiv.org, revised Jul 2022.
• Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
• Omid Safarzadeh, 2020. "Generating Trading Signals by ML algorithms or time series ones?," Papers 2007.11098, arXiv.org.
• Lennart Oelschlager & Timo Adam, 2020. "Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models," Papers 2007.14874, arXiv.org.
• Glocker, Christian & Kaniovski, Serguei, 2020. "Structural modeling and forecasting using a cluster of dynamic factor models," MPRA Paper 101874, University Library of Munich, Germany.
• Peng-Fei Dai & Xiong Xiong & Wei-Xing Zhou, 2020. "The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model," Papers 2007.12838, arXiv.org.
• Okpara, Godwin Chigozie, 2020. "News on Stock Market Returns and Conditional Volatility in Nigeria: An EGARCH-in-Mean Approach," MPRA Paper 102381, University Library of Munich, Germany, revised 12 Aug 2020.