IDEAS home Printed from https://ideas.repec.org/r/eee/econom/v99y2000i1p39-61.html
   My bibliography  Save this item

Consistent cross-validatory model-selection for dependent data: hv-block cross-validation

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Cui, Can & Wu, Teresa & Hu, Mengqi & Weir, Jeffery D. & Li, Xiwang, 2016. "Short-term building energy model recommendation system: A meta-learning approach," Applied Energy, Elsevier, vol. 172(C), pages 251-263.
  2. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
  3. Pinto, Jeronymo Marcondes & Marçal, Emerson Fernandes, 2019. "Cross-validation based forecasting method: a machine learning approach," Textos para discussão 498, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
  4. Filip Stanek, 2021. "Optimal Out-of-Sample Forecast Evaluation under Stationarity," CERGE-EI Working Papers wp712, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
  5. Kock, Anders Bredahl & Teräsvirta, Timo, 2014. "Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009," International Journal of Forecasting, Elsevier, vol. 30(3), pages 616-631.
  6. Mariana Oliveira & Luís Torgo & Vítor Santos Costa, 2021. "Evaluation Procedures for Forecasting with Spatiotemporal Data," Mathematics, MDPI, vol. 9(6), pages 1-27, March.
  7. Scholz, Michael & Nielsen, Jens Perch & Sperlich, Stefan, 2015. "Nonparametric prediction of stock returns based on yearly data: The long-term view," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 143-155.
  8. Benjamin Poignard & Manabu Asaiz, 2020. "A Penalised OLS Framework for High-Dimensional Multivariate Stochastic Volatility Models," Discussion Papers in Economics and Business 20-02, Osaka University, Graduate School of Economics.
  9. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
  10. Momin M. Malik, 2020. "A Hierarchy of Limitations in Machine Learning," Papers 2002.05193, arXiv.org, revised Feb 2020.
  11. Anders Bredahl Kock & Timo Teräsvirta, 2016. "Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1753-1779, December.
  12. Kutateladze, Varlam, 2022. "The kernel trick for nonlinear factor modeling," International Journal of Forecasting, Elsevier, vol. 38(1), pages 165-177.
  13. Muniain, Peru & Ziel, Florian, 2020. "Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1193-1210.
  14. Varlam Kutateladze, 2021. "The Kernel Trick for Nonlinear Factor Modeling," Papers 2103.01266, arXiv.org.
  15. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
  16. Adriano Koshiyama & Nick Firoozye & Philip Treleaven, 2021. "Generative adversarial networks for financial trading strategies fine-tuning and combination," Quantitative Finance, Taylor & Francis Journals, vol. 21(5), pages 797-813, May.
  17. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  18. Gary S. Anderson & Alena Audzeyeva, 2019. "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series 2019-074, Board of Governors of the Federal Reserve System (U.S.).
  19. González Andrés & Teräsvirta Timo, 2008. "Modelling Autoregressive Processes with a Shifting Mean," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(1), pages 1-28, March.
  20. Benjamin Poignard & Manabu Asai, 2023. "High‐dimensional sparse multivariate stochastic volatility models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 4-22, January.
  21. Ye, Xu-Guo & Lin, Jin-Guan & Zhao, Yan-Yong & Hao, Hong-Xia, 2015. "Two-step estimation of the volatility functions in diffusion models with empirical applications," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 135-159.
  22. Patrick Carmack & Jeffrey Spence & William Schucany, 2012. "Generalised correlated cross-validation," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(2), pages 269-282.
  23. Brownlees, Christian T. & Gallo, Giampiero M., 2011. "Shrinkage estimation of semiparametric multiplicative error models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 365-378, April.
  24. Nikolay Gospodinov & Masayuki Hirukawa, 2008. "Nonparametric Estimation of Scalar Diffusion Processes of Interest Rates Using Asymmetric Kernels," Working Papers 08011, Concordia University, Department of Economics, revised Dec 2008.
  25. Heij, C. & Groenen, P.J.F. & van Dijk, D.J.C., 2006. "Time series forecasting by principal covariate regression," Econometric Institute Research Papers EI 2006-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  26. Čížek, Pavel & Koo, Chao Hui, 2021. "Jump-preserving varying-coefficient models for nonlinear time series," Econometrics and Statistics, Elsevier, vol. 19(C), pages 58-96.
  27. Aman Ullah & Huansha Wang, 2013. "Parametric and Nonparametric Frequentist Model Selection and Model Averaging," Econometrics, MDPI, vol. 1(2), pages 1-23, September.
  28. Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
  29. Xiaolu Wei & Hongbing Ouyang, 2023. "Forecasting Carbon Price Using Double Shrinkage Methods," IJERPH, MDPI, vol. 20(2), pages 1-20, January.
  30. Benjamin Poignard & Manabu Asai, 2023. "Estimation of high-dimensional vector autoregression via sparse precision matrix," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 307-326.
  31. Massimiliano Caporin & Francesco Poli, 2017. "Building News Measures from Textual Data and an Application to Volatility Forecasting," Econometrics, MDPI, vol. 5(3), pages 1-46, August.
  32. Deng, Ai, 2023. "Time series cross validation: A theoretical result and finite sample performance," Economics Letters, Elsevier, vol. 233(C).
  33. Nadja Bodner & Laura Bringmann & Francis Tuerlinckx & Peter Jonge & Eva Ceulemans, 2022. "ConNEcT: A Novel Network Approach for Investigating the Co-occurrence of Binary Psychopathological Symptoms Over Time," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 107-132, March.
  34. Christoph Bergmeir & Rob J Hyndman & Bonsoo Koo, 2015. "A Note on the Validity of Cross-Validation for Evaluating Time Series Prediction," Monash Econometrics and Business Statistics Working Papers 10/15, Monash University, Department of Econometrics and Business Statistics.
  35. Johann Pfitzinger, 2021. "An Interpretable Neural Network for Parameter Inference," Papers 2106.05536, arXiv.org.
  36. Pierre Masselot & Fateh Chebana & Taha B. M. J. Ouarda & Diane Bélanger & Pierre Gosselin, 2022. "Data-Enhancement Strategies in Weather-Related Health Studies," IJERPH, MDPI, vol. 19(2), pages 1-13, January.
  37. Tibor Szendrei & Arnab Bhattacharjee & Mark E. Schaffer, 2024. "MIDAS-QR with 2-Dimensional Structure," Papers 2406.15157, arXiv.org.
  38. Buckmann, Marcus & Joseph, Andreas, 2022. "An interpretable machine learning workflow with an application to economic forecasting," Bank of England working papers 984, Bank of England.
  39. Caporin, Massimiliano & Poli, Francesco, 2022. "News and intraday jumps: Evidence from regularization and class imbalance," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
  40. Bergmeir, Christoph & Costantini, Mauro & Benítez, José M., 2014. "On the usefulness of cross-validation for directional forecast evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 132-143.
  41. Roland Langrock & Théo Michelot & Alexander Sohn & Thomas Kneib, 2015. "Semiparametric stochastic volatility modelling using penalized splines," Computational Statistics, Springer, vol. 30(2), pages 517-537, June.
  42. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
  43. Lu, Xun & Su, Liangjun, 2020. "Determining individual or time effects in panel data models," Journal of Econometrics, Elsevier, vol. 215(1), pages 60-83.
  44. Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021. "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 46-61.
  45. Schratz, Patrick & Muenchow, Jannes & Iturritxa, Eugenia & Richter, Jakob & Brenning, Alexander, 2019. "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data," Ecological Modelling, Elsevier, vol. 406(C), pages 109-120.
  46. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
  47. Benjamin Poignard, 2020. "Asymptotic theory of the adaptive Sparse Group Lasso," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 297-328, February.
  48. Kulaksizoglu, Tamer, 2015. "Measuring the Core Inflation in Turkey with the SM-AR Model," MPRA Paper 62653, University Library of Munich, Germany.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.