Report NEP-ECM-2019-05-06

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:

• Arturas Juodis & Yiannis Karavias, 2019. "Partially heterogeneous tests for Granger non-causality in panel data," Bank of Lithuania Working Paper Series 59, Bank of Lithuania.
• Peter C. B. Phillips & Zhentao Shi, 2019. "Boosting: Why You Can Use the HP Filter," Papers 1905.00175, arXiv.org, revised Nov 2020.
• Nick Koning & Paul Bekker, 2019. "Exact Testing of Many Moment Inequalities Against Multiple Violations," Papers 1904.12775, arXiv.org, revised Jun 2020.
• Papageorgiou, Ioulia & Moustaki, Irini, 2019. "Sampling of pairs in pairwise likelihood estimation for latent variable models with categorical observed variables," LSE Research Online Documents on Economics 87592, London School of Economics and Political Science, LSE Library.
• A Clements & D Preve, 2019. "A Practical Guide to Harnessing the HAR Volatility Model," NCER Working Paper Series 120, National Centre for Econometric Research.
• Gregor Zens & Maximilian Bock, 2019. "A Factor-Augmented Markov Switching (FAMS) Model," Papers 1904.13194, arXiv.org, revised May 2019.
• Rico Krueger & Prateek Bansal & Michel Bierlaire & Ricardo A. Daziano & Taha H. Rashidi, 2019. "Variational Bayesian Inference for Mixed Logit Models with Unobserved Inter- and Intra-Individual Heterogeneity," Papers 1905.00419, arXiv.org, revised Jan 2020.
• Jelena Bradic & Stefan Wager & Yinchu Zhu, 2019. "Sparsity Double Robust Inference of Average Treatment Effects," Papers 1905.00744, arXiv.org.
• Ingo Hoffmann & Christoph J. Borner, 2019. "Tail models and the statistical limit of accuracy in risk assessment," Papers 1904.12113, arXiv.org.
• Allison Koenecke & Amita Gajewar, 2019. "Curriculum Learning in Deep Neural Networks for Financial Forecasting," Papers 1904.12887, arXiv.org, revised Jul 2019.
• A Clements & M Doolan, 2018. "Combining Multivariate Volatility Forecasts using Weighted Losses," NCER Working Paper Series 119, National Centre for Econometric Research.
• John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Feb 2021.