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Testing for Granger causality in large mixed-frequency VARs

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  1. is not listed on IDEAS
  2. Gianluca Cubadda & Alain Hecq, 2021. "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper 525, Tor Vergata University, CEIS, revised 08 Nov 2021.
  3. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High‐Dimensional Vector Autoregressive Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(5), pages 1123-1152, October.
  4. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2020. "Testing a large set of zero restrictions in regression models, with an application to mixed frequency Granger causality," Journal of Econometrics, Elsevier, vol. 218(2), pages 633-654.
  5. Martin Enilov, 2024. "The predictive power of commodity prices for future economic growth: Evaluating the role of economic development," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3040-3062, July.
  6. del Barrio Castro, Tomás & Hecq, Alain, 2016. "Testing for deterministic seasonality in mixed-frequency VARs," Economics Letters, Elsevier, vol. 149(C), pages 20-24.
  7. Feifei Huang & Mingxia Lin & Shoukat Iqbal Khattak, 2024. "Form Uncertainty to Sustainable Decision-Making: A Novel MIDAS–AM–DeepAR-Based Prediction Model for E-Commerce Industry Development," Sustainability, MDPI, vol. 16(14), pages 1-24, July.
  8. Gianluca Cubadda & Alain Hecq, 2020. "Dimension Reduction for High Dimensional Vector Autoregressive Models," Papers 2009.03361, arXiv.org, revised Feb 2022.
  9. Dilara Berksun & Nukhet Dogan & M. Hakan Berument, 2021. "Electricity Consumption and Economic Growth in Turkey: A Mixed Frequency Var Approach," Energy Economics Letters, Asian Economic and Social Society, vol. 8(1), pages 95-108, June.
  10. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
  11. Khalaf, Lynda & Kichian, Maral & Saunders, Charles J. & Voia, Marcel, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Journal of Econometrics, Elsevier, vol. 220(2), pages 589-605.
  12. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
  13. Tom Dudda & Tony Klein & Duc Khuong Nguyen & Thomas Walther, 2022. "Common Drivers of Commodity Futures?," Working Papers 2207, Utrecht School of Economics.
  14. Xinzhou Qi & Wenjing Zhang & Ling Sun & Zijiang Hu & Zhong Ning, 2024. "Navigating the tides of uncertainty: exploring the complex relationship between global economic policy and crude oil transportation," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 26(4), pages 612-629, December.
  15. Wang, Zhuo & Wei, Yu & Shang, Yue & Wang, Qian & Zhao, Cheng, 2025. "Do economic policy uncertainties matter for economic growth? Evidence from MIDAS approaches," Research in International Business and Finance, Elsevier, vol. 74(C).
  16. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
  17. Ankargren, Sebastian & Jonéus, Paulina, 2021. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Econometrics and Statistics, Elsevier, vol. 19(C), pages 97-113.
  18. Olatunji Abdul Shobande & Joseph Onuche Enemona, 2021. "A Multivariate VAR Model for Evaluating Sustainable Finance and Natural Resource Curse in West Africa: Evidence from Nigeria and Ghana," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
  19. Andrea Cipollini & Ieva Mikaliunaite, 2021. "Financial distress and real economic activity in Lithuania: a Granger causality test based on mixed-frequency VAR," Empirical Economics, Springer, vol. 61(2), pages 855-881, August.
  20. Alain Hecq & Marie Ternes & Ines Wilms, 2025. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(6), pages 1946-1968, September.
  21. Lu, Wanbo & Liu, Qibo & Wang, Jie, 2024. "Effect of electricity policy uncertainty and carbon emission prices on electricity demand in China based on mixed-frequency data models," Utilities Policy, Elsevier, vol. 91(C).
  22. Motegi, Kaiji & Sadahiro, Akira, 2018. "Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 118-128.
  23. Giusto Andrea & İşcan Talan B., 2018. "The Rescaled VAR Model with an Application to Mixed-Frequency Macroeconomic Forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(4), pages 1-16, September.
  24. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2016. "Testing for Granger causality with mixed frequency data," Journal of Econometrics, Elsevier, vol. 192(1), pages 207-230.
  25. Chi-Wei Su & Yuru Song & Hsu-Ling Chang & Weike Zhang & Meng Qin, 2023. "Could Cryptocurrency Policy Uncertainty Facilitate U.S. Carbon Neutrality?," Sustainability, MDPI, vol. 15(9), pages 1-15, May.
  26. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
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