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Autoregressive process modeling via the Lasso procedure

Citations

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Cited by:

  1. Ziel, Florian, 2016. "Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR–ARCH type processes," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 773-793.
  2. Lucas C Parra & Aimar Silvan & Maximilian Nentwich & Jens Madsen & Vera E Parra & Behtash Babadi, 2025. "VARX Granger analysis: Models for neuroscience, physiology, sociology and econometrics," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-21, January.
  3. Florian Ziel, 2015. "Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes," Papers 1502.06557, arXiv.org, revised Dec 2015.
  4. Francesco Audrino & Lorenzo Camponovo, 2013. "Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models," Papers 1312.1473, arXiv.org.
  5. Chen, Rong & Xiao, Han & Yang, Dan, 2021. "Autoregressive models for matrix-valued time series," Journal of Econometrics, Elsevier, vol. 222(1), pages 539-560.
  6. Alessandro Gregorio & Francesco Iafrate, 2021. "Regularized bridge-type estimation with multiple penalties," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(5), pages 921-951, October.
  7. Marcelo C. Medeiros & Eduardo F. Mendes, 2015. "l1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations," Textos para discussão 636, Department of Economics PUC-Rio (Brazil).
  8. Ling Peng & Yan Zhu & Wenxuan Zhong, 2023. "Lasso regression in sparse linear model with $$\varphi $$ φ -mixing errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(1), pages 1-26, January.
  9. Ruisi Nan & Jingwei Wang & Hanfang Li & Youxi Luo, 2025. "Robust Variable Selection via Bayesian LASSO-Composite Quantile Regression with Empirical Likelihood: A Hybrid Sampling Approach," Mathematics, MDPI, vol. 13(14), pages 1-20, July.
  10. Kock, Anders Bredahl & Callot, Laurent, 2015. "Oracle inequalities for high dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 186(2), pages 325-344.
  11. MArcelo C. Medeiros & Eduardo F.Mendes, 2012. "Estimating High-Dimensional Time Series Models," Textos para discussão 602, Department of Economics PUC-Rio (Brazil).
  12. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
  13. repec:hum:wpaper:sfb649dp2016-047 is not listed on IDEAS
  14. Siddhartha Nandy & Chae Young Lim & Tapabrata Maiti, 2017. "Additive model building for spatial regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 779-800, June.
  15. Kai Yang & Xue Ding & Xiaohui Yuan, 2022. "Bayesian empirical likelihood inference and order shrinkage for autoregressive models," Statistical Papers, Springer, vol. 63(1), pages 97-121, February.
  16. Smeekes, Stephan & Wijler, Etienne, 2018. "Macroeconomic forecasting using penalized regression methods," International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
  17. ALAMI CHENTOUFI, Reda, 2024. "Penalized Convex Estimation in Dynamic Location-Scale models," MPRA Paper 123283, University Library of Munich, Germany.
  18. Adamek, Robert & Smeekes, Stephan & Wilms, Ines, 2023. "Lasso inference for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 235(2), pages 1114-1143.
  19. Nepp, Alexander & Okhrin, Ostap & Egorova, Julia & Dzhuraeva, Zarnigor & Zykov, Alexander, 2022. "What threatens stock markets more - The coronavirus or the hype around it?," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 519-539.
  20. Kascha, Christian & Trenkler, Carsten, 2015. "Forecasting VARs, model selection, and shrinkage," Working Papers 15-07, University of Mannheim, Department of Economics.
  21. Zbonakova, L. & Härdle, W.K. & Wang, W., 2016. "Time Varying Quantile Lasso," Working Papers 16/07, Department of Economics, City St George's, University of London.
  22. Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).
  23. Zbonakova, Lenka & Härdle, Wolfgang Karl & Wang, Weining, 2016. "Time varying quantile Lasso," SFB 649 Discussion Papers 2016-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  24. Garofalo, Antonio & Bruno, Emma & D'Avino, Maria & Ferraro, Aniello & Punzo, Gennaro, 2025. "Employee compensation in European regions: A spatial analysis of short- and long-term effects," Structural Change and Economic Dynamics, Elsevier, vol. 74(C), pages 303-314.
  25. Xinyang Wang & Dehui Wang & Kai Yang, 2021. "Integer-valued time series model order shrinkage and selection via penalized quasi-likelihood approach," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 713-750, July.
  26. Zhifeng Dai & Tingyu Li & Mi Yang, 2022. "Forecasting stock return volatility: The role of shrinkage approaches in a data‐rich environment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 980-996, August.
  27. Maxime Faymonville & Carsten Jentsch & Christian H. Weiß & Boris Aleksandrov, 2023. "Semiparametric estimation of INAR models using roughness penalization," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 365-400, June.
  28. Marcelo C. Medeiros & Eduardo F. Mendes, 2017. "Adaptive LASSO estimation for ARDL models with GARCH innovations," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 622-637, October.
  29. Francesco Audrino & Simon D. Knaus, 2016. "Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1485-1521, December.
  30. Sander Barendse, 2023. "Expected Shortfall LASSO," Papers 2307.01033, arXiv.org, revised Jan 2024.
  31. Xinyang Wang & Dehui Wang & Haixiang Zhang, 2020. "Poisson autoregressive process modeling via the penalized conditional maximum likelihood procedure," Statistical Papers, Springer, vol. 61(1), pages 245-260, February.
  32. Medeiros, Marcelo C. & Mendes, Eduardo F., 2016. "ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 191(1), pages 255-271.
  33. Jakub Bandurski & Eliza Hałatek & Adam Łaziński & Michał Künstler, 2025. "Is smooth Energiewende possible? Improving the performance of climate policies in Germany by optimizing the risk of electricity delivery," Working Papers 2025-30, Faculty of Economic Sciences, University of Warsaw.
  34. Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
  35. Chor-yiu Sin & Shu-Hui Yu, 2019. "Order selection for possibly infinite-order non-stationary time series," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(2), pages 187-216, June.
  36. Hamed Haselimashhadi & Veronica Vinciotti, 2018. "Penalised inference for lagged dependent regression in the presence of autocorrelated residuals," METRON, Springer;Sapienza Università di Roma, vol. 76(1), pages 49-68, April.
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