References
- Acemoglu, D. and P. Restrepo (2020), "Robots and Jobs: Evidence from US Labor Markets", Journal of Political Economy, 128(6), 2188-2244. https://doi.org/10.1086/705716
- Agrawal, A., J. Gans and A. Goldfarb (2018), Prediction Machines: The Simple Economics of Artificial Intelligence, Harvard Business Review Press.
- Athey, S. (2017), "Beyond Prediction: Using Big Data for Policy Problems", Science, 355(6324), 483-485. https://doi.org/10.1126/science.aal4321
- Athey, S. (2019), The Impact of Machine Learning on Economics, In The Economics of Artificial Intelligence: An Agenda, 1 Edition, 507-547, National Bureau of Economic Research Conference Report, University of Chicago Press.
- Athey, S., J. Tibshirani and S. Wager (2019), "Generalized Random Forests", The Annals of Statistics, 47(2), 1148-1178.
- Chalfin, A., O. Danieli, A. Hillis, Z. Jelveh, M. Luca, J. Ludwig et al. (2016), "Productivity and Selection of Human Capital with Machine Learning", American Economic Review, 106(5), 124-127. https://doi.org/10.1257/aer.p20161029
- Chakraborty, C. and A. Joseph (2017), "Machine Learning at Central Banks", Bank of Eng-Land Staff Working Paper, No. 674.
- De Prado, M. L. (2018), Advances in Financial Machine Learning, New York, NY, USA: Wiley.
- Ding, M. J., S. Z. Zhang, H. D. Zhong, Y. H. Wu and L. B. Zhang (2019), "A Prediction Model of the Sum of Container Based on Combined BP Neural Network and SVM", Journal of Information Processing Systems, 15(2), 305-319.
- Geron, A. (2017), Hands-On Machine Learning with Scikit-Learn and Tensor Flow: Concepts, Tools, and Techniques to Build Intelligent Systems, 1st Edition, O'Reilly Media.
- Gu, S., B. Kelly and D. Xiu (2019), "Empirical Asset Pricing via Machine Learning", NBER Working Paper No. 25398.
- Hastie, T., R. Tibshirani and J. Fridman (2017), The Elements of Statistical Learning, Second Edition, Springer.
- Jean, N., M. Burke, M. Xie, W. M. Davis, D. B. Lobell and S. Ermon (2016), "Combining Satellite Imagery and Machine Learning to Predict Poverty", Science, 353(6301), 790-794. https://doi.org/10.1126/science.aaf7894
- Kim, Soo-Hyon (2020), "Macroeconomic and Financial Market Analyses and Predictions through Deep Learning", Bank of Korea Working Paper, No. 2020-18.
- Kreif, N. and K. DiazOrdaz (2019), Machine Learning in Policy Evaluation: New Tools for Causal Inference, In Oxford Research Encyclopedia of Economics and Finance, by Noemi Kreif and Karla DiazOrdaz, Oxford University Press.
- Mullainathan, S. and J. Spiess (2017), "Machine Learning: An Applied Econometric Approach", Journal of Economic Perspectives, 31(2), 87-106. https://doi.org/10.1257/jep.31.2.87
- Nielsen, M. A. (2015), Neural Networks and Deep Learning, San Francisco, Determination Press.
- Peysakhovich, A. and J. Naecker (2017), "Using Methods from Machine Learning to Evaluate Behavioral Models of Choice under Risk and Ambiguity", Journal of Economic Behavior & Organization, 133, 373-384. https://doi.org/10.1016/j.jebo.2016.08.017
- Schlkopf, B. and A. J. Smola (2001), Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 1st Edition, Adaptive Computation and Machine Learning Series, The MIT Press.
- Varian, H. R. (2014), "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, 28(2), 3-28. https://doi.org/10.1257/jep.28.2.3
- Yi, Chae-Deug and Young-Woo Lee (2020), "Busan's Economy Prediction and Strategic Industry Using Machine Learning in Artificial Intelligence", Bank of Korea, Busan.
- Zou, H. and T. Hastie (2005), "Regularization and Variable Selection via the Elastic Net", Journal of the Royal Statistical Society, Series B(Statistical Methodology), 67(2), 301-320. https://doi.org/10.1111/j.1467-9868.2005.00503.x