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Report NEP-ECM-2021-02-08
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:
- Shi, Chengchun & Song, Rui & Lu, Wenbin & Li, Runzi, 2020, "Statistical inference for high-dimensional models via recursive online-score estimation," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 103043, Jan.
- Paulo M.M. Rodrigues & Marina Balboa, 2021, "Multivariate Fractional Integration Tests allowing for Conditional Heteroskedasticity with an Application to Return Volatility and Trading Volume," Working Papers, Banco de Portugal, Economics and Research Department, number w202102.
- Andre Lucas & Anne Opschoor & Luca Rossini, 2021, "Tail Heterogeneity for Dynamic Covariance Matrices: the F-Riesz Distribution," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 21-010/III, Jan, revised 11 Jul 2023.
- Wang, Wenjie, 2020, "On Bootstrap Validity for the Test of Overidentifying Restrictions with Many Instruments and Heteroskedasticity," MPRA Paper, University Library of Munich, Germany, number 104858, Dec.
- Siem Jan Koopman & Julia Schaumburg & Quint Wiersma, 2021, "Joint Modelling and Estimation of Global and Local Cross-Sectional Dependence in Large Panels," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 21-008/III, Jan.
- Pincheira, Pablo & Hardy, Nicolás & Muñoz, Felipe, 2021, ""Go wild for a while!": A new asymptotically Normal test for forecast evaluation in nested models," MPRA Paper, University Library of Munich, Germany, number 105368, Jan.
- Minji Bang & Wayne Yuan Gao & Andrew Postlewaite & Holger Sieg, 2021, "Using Monotonicity Restrictions to Identify Models with Partially Latent Covariates," Papers, arXiv.org, number 2101.05847, Jan, revised Jun 2022.
- Palumbo, D., 2021, "Testing and Modelling Time Series with Time Varying Tails," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2111, Jan.
- Nicolas Debarsy & Cem Ertur, 2019, "Interaction matrix selection in spatial econometrics with an application to growth theory," Post-Print, HAL, number halshs-01278545, Mar, DOI: 10.1016/j.regsciurbeco.2019.01.002.
- Florian Eckert & Philipp Kronenberg & Heiner Mikosch & Stefan Neuwirth, 2020, "Tracking Economic Activity With Alternative High-Frequency Data," KOF Working papers, KOF Swiss Economic Institute, ETH Zurich, number 20-488, Dec, DOI: 10.3929/ethz-b-000458723.
- Edvard Bakhitov & Amandeep Singh, 2021, "Causal Gradient Boosting: Boosted Instrumental Variable Regression," Papers, arXiv.org, number 2101.06078, Jan.
- Arthur Lewbel & Susanne M. Schennach & Linqi Zhang, 2020, "Identification of a Triangular Two Equation System Without Instruments," Boston College Working Papers in Economics, Boston College Department of Economics, number 1022, Dec, revised 15 Dec 2022.
- Tam'as Krisztin & Philipp Piribauer, 2021, "A Bayesian approach for estimation of weight matrices in spatial autoregressive models," Papers, arXiv.org, number 2101.11938, Jan, revised Aug 2022.
- Pablo Montero-Manso & Rob J Hyndman, 2020, "Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 45/20.
- Daouia, Abdelaati & Gijbels, Irene & Stupfler, Gilles, 2021, "Extremile Regression," TSE Working Papers, Toulouse School of Economics (TSE), number 21-1176, Jan.
- Pincheira, Pablo & Hardy, Nicolas, 2020, "The Mean Squared Prediction Error Paradox: A summary," MPRA Paper, University Library of Munich, Germany, number 105020, Dec.