Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms
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- Yongchen Zhao, 2021. "The robustness of forecast combination in unstable environments: a Monte Carlo study of advanced algorithms," Empirical Economics, Springer, vol. 61(1), pages 173-199, July.
- Yongchen Zhao, 2015. "Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms," Working Papers 2015-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
References listed on IDEAS
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Cited by:
- Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2016.
"Everything you always wanted to know about bitcoin modelling but were afraid to ask. I,"
Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 44, pages 5-24.
- Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask," MPRA Paper 71946, University Library of Munich, Germany, revised 2016.
- Constantin Bürgi & Tara M. Sinclair, 2017.
"A nonparametric approach to identifying a subset of forecasters that outperforms the simple average,"
Empirical Economics, Springer, vol. 53(1), pages 101-115, August.
- Constantin Bürgi & Tara M. Sinclair, 2015. "A Nonparametric Approach to Identifying a Subset of Forecasters that Outperforms the Simple Average," Working Papers 2015-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
- Yongchen Zhao, 2020.
"Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set,"
Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 77-97, November.
- Herman Stekler & Yongchen Zhao, 2016. "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Working Papers 2016-15, Towson University, Department of Economics, revised Sep 2016.
- Herman O. Stekler & Yongchen Zhao, 2016. "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Working Papers 2016-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
- Constantin Rudolf Salomo Bürgi, 2023.
"How to deal with missing observations in surveys of professional forecasters,"
Journal of Applied Economics, Taylor & Francis Journals, vol. 26(1), pages 2185975-218, December.
- Constantin Bürgi, 2023. "How to Deal With Missing Observations in Surveys of Professional Forecasters," CESifo Working Paper Series 10203, CESifo.
- Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2017. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. Part 2," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 45, pages 5-28.
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More about this item
Keywords
Exponential re-weighting; Shrinkage; Estimation error; Performance instability.;All these keywords.
JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2015-12-28 (Econometrics)
- NEP-FOR-2015-12-28 (Forecasting)
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