Details about Ines Wilms
Access statistics for papers by Ines Wilms.
Last updated 2021-07-05. Update your information in the RePEc Author Service.
Short-id: pwi441
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Working Papers
2022
- Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions
Papers, arXiv.org View citations (1)
- Lasso Inference for High-Dimensional Time Series
Papers, arXiv.org View citations (1)
2021
- Tree-based Node Aggregation in Sparse Graphical Models
Papers, arXiv.org
- bootUR: An R Package for Bootstrap Unit Root Tests
Papers, arXiv.org
2018
- White heteroscedasticty testing after outlier removal
Economics Series Working Papers, University of Oxford, Department of Economics View citations (5)
2017
- Cellwise robust regularized discriminant analysis
Working Papers of Department of Decision Sciences and Information Management, Leuven, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven
- Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach
Papers, arXiv.org 
Also in Working Papers of Department of Decision Sciences and Information Management, Leuven, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven (2017) View citations (1)
2016
- Commodity Dynamics: A Sparse Multi-class Approach
Papers, arXiv.org View citations (6)
Also in Working Papers of Department of Decision Sciences and Information Management, Leuven, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven (2016) 
See also Journal Article in Energy Economics (2016)
- Lasso-based forecast combinations for forecasting realized variances
Working Papers of Department of Decision Sciences and Information Management, Leuven, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven
- Multi-class vector autoregressive models for multi-store sales data
Working Papers of Department of Decision Sciences and Information Management, Leuven, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven 
See also Journal Article in Journal of the Royal Statistical Society Series C (2018)
2015
- An algorithm for the multivariate group lasso with covariance estimation
Working Papers of Department of Decision Sciences and Information Management, Leuven, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven 
See also Journal Article in Journal of Applied Statistics (2018)
- The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach
Working Papers of Department of Decision Sciences and Information Management, Leuven, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven 
See also Journal Article in European Journal of Operational Research (2016)
2014
- Robust sparse canonical correlation analysis
Working Papers of Department of Decision Sciences and Information Management, Leuven, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven
Journal Articles
2021
- Heteroscedasticity testing after outlier removal
Econometric Reviews, 2021, 40, (1), 51-85
- Multivariate volatility forecasts for stock market indices
International Journal of Forecasting, 2021, 37, (2), 484-499 View citations (4)
2020
- Volatility spillovers in commodity markets: A large t-vector autoregressive approach
Energy Economics, 2020, 85, (C) View citations (15)
2018
- An algorithm for the multivariate group lasso with covariance estimation
Journal of Applied Statistics, 2018, 45, (4), 668-681 View citations (2)
See also Working Paper (2015)
- Multiclass vector auto‐regressive models for multistore sales data
Journal of the Royal Statistical Society Series C, 2018, 67, (2), 435-452 View citations (1)
See also Working Paper (2016)
2016
- Commodity dynamics: A sparse multi-class approach
Energy Economics, 2016, 60, (C), 62-72 View citations (6)
See also Working Paper (2016)
- Discussion of ‘Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models’
Scandinavian Journal of Statistics, 2016, 43, (2), 353-356
- Forecasting using sparse cointegration
International Journal of Forecasting, 2016, 32, (4), 1256-1267 View citations (17)
- Identifying Demand Effects in a Large Network of Product Categories
Journal of Retailing, 2016, 92, (1), 25-39 View citations (12)
- The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach
European Journal of Operational Research, 2016, 254, (1), 138-147 View citations (12)
See also Working Paper (2015)
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