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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. Yong Yin issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
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, Cowles Foundation for Research in Economics, Yale University, number 2250, Aug.
- Kwok, Simon, 2020, "Nonparametric Inference of Jump Autocorrelation," Working Papers, University of Sydney, School of Economics, number 2020-09, Aug, revised Jan 2021.
- Ke Miao & Peter C.B. Phillips & Liangjun Su, 2020, "High-Dimensional VARs with Common Factors," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2252, Aug.
- Fantazzini, Dean, 2020, "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," MPRA Paper, University Library of Munich, Germany, number 102315, Aug.
- Ruijun Bu & Jihyun Kim & Bin Wang, 2020, "Uniform and Lp Convergences of Nonparametric Estimation for Diffusion Models," Working Papers, University of Liverpool, Department of Economics, number 202021, Jul.
- Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2020, "Nowcasting with large Bayesian vector autoregressions," Working Paper Series, European Central Bank, number 2453, Aug.
- Ye Chen & Peter C.B. Phillips & Shuping Shi, 2020, "Common Bubble Detection in Large Dimensional Financial Systems," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2251, Aug.
- Jos'e Luis Montiel Olea & Mikkel Plagborg-M{o}ller, 2020, "Local Projection Inference is Simpler and More Robust Than You Think," Papers, arXiv.org, number 2007.13888, Jul, revised Jan 2026.
- Federico A. Bugni & Jia Li & Qiyuan Li, 2020, "Permutation-based tests for discontinuities in event studies," Papers, arXiv.org, number 2007.09837, Jul, 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, KOF Swiss Economic Institute, ETH Zurich, number 20-472, May, DOI: 10.3929/ethz-b-000399304.
- Omid Safarzadeh, 2020, "Generating Trading Signals by ML algorithms or time series ones?," Papers, arXiv.org, number 2007.11098, Jul.
- Lennart Oelschlager & Timo Adam, 2020, "Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models," Papers, arXiv.org, number 2007.14874, Jul.
- Glocker, Christian & Kaniovski, Serguei, 2020, "Structural modeling and forecasting using a cluster of dynamic factor models," MPRA Paper, University Library of Munich, Germany, number 101874, Jul.
- 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, arXiv.org, number 2007.12838, Jul.
- Okpara, Godwin Chigozie, 2020, "News on Stock Market Returns and Conditional Volatility in Nigeria: An EGARCH-in-Mean Approach," MPRA Paper, University Library of Munich, Germany, number 102381, Aug, revised 12 Aug 2020.