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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.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
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, Bank of Lithuania, number 59, Apr.
- Peter C. B. Phillips & Zhentao Shi, 2019, "Boosting: Why You Can Use the HP Filter," Papers, arXiv.org, number 1905.00175, Apr, revised Nov 2020.
- Nick Koning & Paul Bekker, 2019, "Exact Testing of Many Moment Inequalities Against Multiple Violations," Papers, arXiv.org, number 1904.12775, Apr, 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, London School of Economics and Political Science, LSE Library, number 87592, Mar.
- A Clements & D Preve, 2019, "A Practical Guide to Harnessing the HAR Volatility Model," NCER Working Paper Series, National Centre for Econometric Research, number 120, Apr.
- Gregor Zens & Maximilian Bock, 2019, "A Factor-Augmented Markov Switching (FAMS) Model," Papers, arXiv.org, number 1904.13194, Apr, 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, arXiv.org, number 1905.00419, May, revised Jan 2020.
- Jelena Bradic & Stefan Wager & Yinchu Zhu, 2019, "Sparsity Double Robust Inference of Average Treatment Effects," Papers, arXiv.org, number 1905.00744, May.
- Ingo Hoffmann & Christoph J. Borner, 2019, "Tail models and the statistical limit of accuracy in risk assessment," Papers, arXiv.org, number 1904.12113, Apr.
- Allison Koenecke & Amita Gajewar, 2019, "Curriculum Learning in Deep Neural Networks for Financial Forecasting," Papers, arXiv.org, number 1904.12887, Apr, revised Jul 2019.
- A Clements & M Doolan, 2018, "Combining Multivariate Volatility Forecasts using Weighted Losses," NCER Working Paper Series, National Centre for Econometric Research, number 119, Dec.
- John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019, "Supervised Machine Learning for Eliciting Individual Demand," Papers, arXiv.org, number 1904.13329, Apr, revised Feb 2021.