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Details about Ines Wilms

E-mail:
Homepage:https://www.maastrichtuniversity.nl/i.wilms
Workplace:Vakgroep Kwantitatieve Economie (Department of Quantitative Economics), School of Business and Economics, Maastricht University, (more information at EDIRC)

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

  1. Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions
    Papers, arXiv.org Downloads View citations (1)
  2. Lasso Inference for High-Dimensional Time Series
    Papers, arXiv.org Downloads View citations (1)

2021

  1. Tree-based Node Aggregation in Sparse Graphical Models
    Papers, arXiv.org Downloads
  2. bootUR: An R Package for Bootstrap Unit Root Tests
    Papers, arXiv.org Downloads

2018

  1. White heteroscedasticty testing after outlier removal
    Economics Series Working Papers, University of Oxford, Department of Economics Downloads View citations (5)

2017

  1. 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 Downloads
  2. Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach
    Papers, arXiv.org Downloads
    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) Downloads View citations (1)

2016

  1. Commodity Dynamics: A Sparse Multi-class Approach
    Papers, arXiv.org Downloads 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) Downloads

    See also Journal Article in Energy Economics (2016)
  2. 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 Downloads
  3. 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 Downloads
    See also Journal Article in Journal of the Royal Statistical Society Series C (2018)

2015

  1. 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 Downloads
    See also Journal Article in Journal of Applied Statistics (2018)
  2. 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 Downloads
    See also Journal Article in European Journal of Operational Research (2016)

2014

  1. 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 Downloads

Journal Articles

2021

  1. Heteroscedasticity testing after outlier removal
    Econometric Reviews, 2021, 40, (1), 51-85 Downloads
  2. Multivariate volatility forecasts for stock market indices
    International Journal of Forecasting, 2021, 37, (2), 484-499 Downloads View citations (4)

2020

  1. Volatility spillovers in commodity markets: A large t-vector autoregressive approach
    Energy Economics, 2020, 85, (C) Downloads View citations (15)

2018

  1. An algorithm for the multivariate group lasso with covariance estimation
    Journal of Applied Statistics, 2018, 45, (4), 668-681 Downloads View citations (2)
    See also Working Paper (2015)
  2. Multiclass vector auto‐regressive models for multistore sales data
    Journal of the Royal Statistical Society Series C, 2018, 67, (2), 435-452 Downloads View citations (1)
    See also Working Paper (2016)

2016

  1. Commodity dynamics: A sparse multi-class approach
    Energy Economics, 2016, 60, (C), 62-72 Downloads View citations (6)
    See also Working Paper (2016)
  2. Discussion of ‘Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models’
    Scandinavian Journal of Statistics, 2016, 43, (2), 353-356 Downloads
  3. Forecasting using sparse cointegration
    International Journal of Forecasting, 2016, 32, (4), 1256-1267 Downloads View citations (17)
  4. Identifying Demand Effects in a Large Network of Product Categories
    Journal of Retailing, 2016, 92, (1), 25-39 Downloads View citations (12)
  5. 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 Downloads View citations (12)
    See also Working Paper (2015)
 
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