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Estimating MIDAS regressions via OLS with polynomial parameter profiling

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Cited by:

  1. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
  2. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
  3. Babii, Andrii & Ball, Ryan T. & Ghysels, Eric & Striaukas, Jonas, 2023. "Machine learning panel data regressions with heavy-tailed dependent data: Theory and application," Journal of Econometrics, Elsevier, vol. 237(2).
  4. V. Candila & O. Cepni & G. Gallo & R. Gupta, 2024. "Influence of Local and Global Economic Policy Uncertainty on the volatility of US state-level equity returns: Evidence from a GARCH-MIDAS approach with Shrinkage and Cluster Analysis," Working Paper CRENoS 202414, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  5. Lixiong Yang, 2022. "Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors," Empirical Economics, Springer, vol. 62(2), pages 533-551, February.
  6. Eunjeong Choi & Soohwan Cho & Dong Keun Kim, 2020. "Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability," Sustainability, MDPI, vol. 12(3), pages 1-14, February.
  7. An, Yimeng & Dang, Yaoguo & Wang, Junjie & Zhou, Huimin & Mai, Son T., 2024. "Mixed-frequency data Sampling Grey system Model: Forecasting annual CO2 emissions in China with quarterly and monthly economic-energy indicators," Applied Energy, Elsevier, vol. 370(C).
  8. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2024. "Nowcasting Norwegian household consumption with debit card transaction data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1220-1244, November.
  9. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2024. "Panel data nowcasting: The case of price–earnings ratios," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 292-307, March.
  10. Liu, Liping & Lü, Zheng & Yoon, Seong-Min, 2025. "Impact of policy uncertainty on stock market volatility in the China’s low-carbon economy," Energy Economics, Elsevier, vol. 141(C).
  11. Qifa Xu & Junqing Zuo & Cuixia Jiang & Yaoyao He, 2021. "A large constrained time‐varying portfolio selection model with DCC‐MIDAS: Evidence from Chinese stock market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3417-3435, July.
  12. Ana Beatriz Galvão & Michael Owyang, 2022. "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1314-1333, November.
  13. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
  14. Maghyereh Aktham & Sweidan Osama & Awartani Basel, 2020. "Asymmetric Responses of Economic Growth to Daily Oil Price Changes: New Global Evidence from Mixed-data Sampling Approach," Review of Economics, De Gruyter, vol. 71(2), pages 81-99, August.
  15. Chulyoung Cho & Jinseok Yang & Beakcheol Jang, 2024. "Heterogeneous macroeconomic factors’ effects on stocks across sizes, styles, and sectors in the South Korean market," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-23, April.
  16. Raffaele Mattera & Michelangelo Misuraca & Maria Spano & Germana Scepi, 2023. "Mixed frequency composite indicators for measuring public sentiment in the EU," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2357-2382, June.
  17. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 605-635.
  18. Wei Miao & Jad Beyhum & Jonas Striaukas & Ingrid Van Keilegom, 2025. "High-dimensional censored MIDAS logistic regression for corporate survival forecasting," Papers 2502.09740, arXiv.org, revised Feb 2026.
  19. Vo, Michael, 2026. "Priced to Perfection? Subjective Expectations in Financial Markets," Other publications TiSEM db4f8097-275c-44bc-9e72-7, Tilburg University, School of Economics and Management.
  20. Ankargren, Sebastian & Jonéus, Paulina, 2021. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Econometrics and Statistics, Elsevier, vol. 19(C), pages 97-113.
  21. Xiafei Li & Chao Liang & Feng Ma, 2025. "Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model," Annals of Operations Research, Springer, vol. 352(3), pages 613-652, September.
  22. Simon Tranberg Bodilsen & Asger Lunde, 2025. "Exploiting News Analytics for Volatility Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(1), pages 18-36, January.
  23. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2021. "Choosing the frequency of volatility components within the Double Asymmetric GARCH–MIDAS–X model," Econometrics and Statistics, Elsevier, vol. 20(C), pages 12-28.
  24. Amendola, A. & Candila, V. & Cipollini, F. & Gallo, G.M., 2024. "Doubly multiplicative error models with long- and short-run components," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
  25. Rong Fu & Luze Xie & Tao Liu & Juan Huang & Binbin Zheng, 2022. "Chinese Economic Growth Projections Based on Mixed Data of Carbon Emissions under the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
  26. Nyberg, Henri & Savva, Christos S., 2026. "Risk-return trade-off in international stock returns: Skewness and business cycles," Econometrics and Statistics, Elsevier, vol. 37(C), pages 42-60.
  27. Niko Hauzenberger Massimiliano Marcellino Michael Pfarrhofer Anna Stelzer, 2026. "Direct Gaussian Process Predictive Regressions with Mixed Frequency Data," BAFFI CAREFIN Working Papers 26265, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
  28. Vincenzo Candila & Giampiero M. Gallo & Lea Petrella, 2020. "Mixed--frequency quantile regressions to forecast Value--at--Risk and Expected Shortfall," Papers 2011.00552, arXiv.org, revised Mar 2023.
  29. Goldmann, Leonie & Crook, Jonathan & Calabrese, Raffaella, 2024. "A new ordinal mixed-data sampling model with an application to corporate credit rating levels," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1111-1126.
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