Form Uncertainty to Sustainable Decision-Making: A Novel MIDAS–AM–DeepAR-Based Prediction Model for E-Commerce Industry Development
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- Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
- Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006.
"Predicting volatility: getting the most out of return data sampled at different frequencies,"
Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," NBER Working Papers 10914, National Bureau of Economic Research, Inc.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
- Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
- Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
- Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016.
"Testing for Granger causality in large mixed-frequency VARs,"
Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
- Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
- Götz, T.B. & Hecq, A.W. & Smeekes, S., 2015. "Testing for Granger Causality in Large Mixed-Frequency VARs," Research Memorandum 036, Maastricht University, Graduate School of Business and Economics (GSBE).
- Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
- Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
- Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015.
"Measuring Uncertainty,"
American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
- Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2013. "Measuring Uncertainty," NBER Working Papers 19456, National Bureau of Economic Research, Inc.
- Shahriar Akter & Samuel Fosso Wamba, 2016. "Big data analytics in E-commerce: a systematic review and agenda for future research," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 173-194, May.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Roberto S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Surajit Bag & Shivam Gupta & Ajay Kumar & Uthayasankar Sivarajah, 2021. "An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance," Post-Print hal-03188195, HAL.
- P. Gagliardini & E. Ghysels & M. Rubin, 2017.
"Indirect Inference Estimation of Mixed Frequency Stochastic Volatility State Space Models using MIDAS Regressions and ARCH Models,"
Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 509-560.
- Patrick Gagliardini & Eric Ghysels & Mirco Rubin, 2016. "Indirect Inference Estimation of Mixed Frequency Stochastic Volatility State Space Models Using MIDAS Regressions and ARCH Models," Swiss Finance Institute Research Paper Series 16-46, Swiss Finance Institute.
- Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010.
"Regression models with mixed sampling frequencies,"
Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
- Elena Andreou, Eric Ghysels & Eric Ghysels & Andros Kourtellos, 2007. "Regression Models with Mixed Sampling Frequencies," University of Cyprus Working Papers in Economics 8-2007, University of Cyprus Department of Economics.
- Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
- Elena Andreou & Patrick Gagliardini & Eric Ghysels & Mirco Rubin, 2020. "Mixed-Frequency Macro–Finance Factor Models: Theory and Applications," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 585-628.
- Xu, Qifa & Niu, Xufeng & Jiang, Cuixia & Huang, Xue, 2015. "The Phillips curve in the US: A nonlinear quantile regression approach," Economic Modelling, Elsevier, vol. 49(C), pages 186-197.
- Hany Fahmy, 2021. "How technological emergence, saturation, and rejuvenation are re-shaping the e-commerce landscape and disrupting consumption? A time series analysis," Applied Economics, Taylor & Francis Journals, vol. 53(6), pages 742-759, February.
- Ping Li & Mohan Menon & Zuoming Liu, 2019. "Green innovation under uncertainty - a dynamic perspective," International Journal of Services, Economics and Management, Inderscience Enterprises Ltd, vol. 10(1), pages 68-88.
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Keywords
sustainable development; e-commerce; industrial economy; uncertainty prediction; mixing frequency data; MIDAS–AM–DeepAR model;All these keywords.
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