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Report NEP-ECM-2022-11-28
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:
- Matteo Barigozzi, 2022, "Principal Component Analysis for High-Dimensional Approximate Factor Models in Time Series: Assumptions, Asymptotic Theory, and Identification," Papers, arXiv.org, number 2211.01921, Nov, revised Feb 2026.
- Yiren Wang & Liangjun Su & Yichong Zhang, 2022, "Low-rank Panel Quantile Regression: Estimation and Inference," Papers, arXiv.org, number 2210.11062, Oct.
- Anish Agarwal & Sarah H. Cen & Devavrat Shah & Christina Lee Yu, 2022, "Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference," Papers, arXiv.org, number 2210.11355, Oct, revised Oct 2023.
- Xiaolin Sun, 2022, "Estimation of Heterogeneous Treatment Effects Using a Conditional Moment Based Approach," Papers, arXiv.org, number 2210.15829, Oct, revised Oct 2024.
- Gregory Cox, 2022, "Weak Identification in Low-Dimensional Factor Models with One or Two Factors," Papers, arXiv.org, number 2211.00329, Nov, revised Mar 2024.
- Philipp Ketz, 2022, "Allowing for weak identification when testing GARCH-X type models," Papers, arXiv.org, number 2210.11398, Oct.
- Rub'en Loaiza-Maya & Didier Nibbering, 2022, "Efficient variational approximations for state space models," Papers, arXiv.org, number 2210.11010, Oct, revised Jun 2023.
- Christian Brownlees & Vladislav Morozov, 2022, "Unit Averaging for Heterogeneous Panels," Papers, arXiv.org, number 2210.14205, Oct, revised May 2024.
- Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022, "Eigenvalue tests for the number of latent factors in short panels," Papers, arXiv.org, number 2210.16042, Oct.
- Item repec:hal:wpaper:hal-03831210 is not listed on IDEAS anymore
- Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022, "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers, arXiv.org, number 2211.00363, Nov, revised Jan 2024.
- Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022, "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper, Federal Reserve Bank of Atlanta, number 2022-16, Nov, DOI: 10.29338/wp2022-16.
- Ekaterina Morozova & Vladimir Panov, 2022, "Modelling the Bitcoin prices and the media attention to Bitcoin via the jump-type processes," Papers, arXiv.org, number 2210.13824, Oct.
- David Childers & Jesús Fernández-Villaverde & Jesse Perla & Christopher Rackauckas & Peifan Wu, 2022, "Differentiable State-Space Models and Hamiltonian Monte Carlo Estimation," NBER Working Papers, National Bureau of Economic Research, Inc, number 30573, Oct.
- Hudde, Ansgar & Jacob, Marita, 2022, "There’s More in the Data! Using Month-Specific Information to Estimate Changes Before and After Major Life Events," SocArXiv, Center for Open Science, number vueas, Oct, DOI: 10.31219/osf.io/vueas.
- Efrem Castelnuovo & Lorenzo Mori, 2022, "Uncertainty, Skewness and the Business Cycle - Through the MIDAS Lens," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2022-69, Oct.
- Alessandra Canepa, & Karanasos, Menelaos & Paraskevopoulos, Athanasios & Chini, Emilio Zanetti, 2022, "Forecasting Ination: A GARCH-in-Mean-Level Model with Time Varying Predictability," Department of Economics and Statistics Cognetti de Martiis. Working Papers, University of Turin, number 202212, Sep.
- Aeppli, Clem & Ruedin, Didier, 2022, "How to Measure Agreement, Consensus, and Polarization in Ordinal Data," SocArXiv, Center for Open Science, number syzbr, Oct, DOI: 10.31219/osf.io/syzbr.
- Richard T. Baillie & Francis X. Diebold & George Kapetanios & Kun Ho Kim, 2022, "A New Test for Market Efficiency and Uncovered Interest Parity," NBER Working Papers, National Bureau of Economic Research, Inc, number 30638, Nov.
- Max Nendel & Alessandro Sgarabottolo, 2022, "A parametric approach to the estimation of convex risk functionals based on Wasserstein distance," Papers, arXiv.org, number 2210.14340, Oct, revised Aug 2024.
- Brodeur, Abel & Cook, Nikolai & Hartley, Jonathan & Heyes, Anthony, 2022, "Do Pre-Registration and Pre-analysis Plans Reduce p-Hacking and Publication Bias?," MetaArXiv, Center for Open Science, number uxf39, Aug, DOI: 10.31219/osf.io/uxf39.