Report NEP-ETS-2022-02-21

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.

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Other reports in NEP-ETS

The following items were announced in this report:

• Luke De Clerk & Sergey Savl'ev, 2022. "A machine learning search for optimal GARCH parameters," Papers 2201.03286, arXiv.org.
• Ke-Li Xu & Junjie Guo, 2021. "A New Test for Multiple Predictive Regression," CAEPR Working Papers 2022-001 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
• Debasis Kundu, 2021. "Stationary GE-Process and its Application in Analyzing Gold Price Data," Papers 2201.02568, arXiv.org.
• Patrik Guggenberge & Frank Kleibergen & Sophocles Mavroeidis, 2022. "A Test for Kronecker Product Structure Covariance Matrix," Economics Series Working Papers 962, University of Oxford, Department of Economics.
• Chen, Yunxiao & Li, Xiaoou, 2022. "Determining the number of factors in high-dimensional generalized latent factor models," LSE Research Online Documents on Economics 111574, London School of Economics and Political Science, LSE Library.
• Kristoffer Pons Bertelsen, 2022. "The Prior Adaptive Group Lasso and the Factor Zoo," CREATES Research Papers 2022-05, Department of Economics and Business Economics, Aarhus University.
• Kenichi Shimizu, 2022. "Asymptotic properties of Bayesian inference in linear regression with a structural break," Working Papers 2022_05, Business School - Economics, University of Glasgow.
• Chen, Yudong & Wang, Tengyao & Samworth, Richard J., 2022. "High-dimensional, multiscale online changepoint detection," LSE Research Online Documents on Economics 113665, London School of Economics and Political Science, LSE Library.