Machine learning and traditional econometric models : a systematic mapping study

• Autorzy: Pérez-Pons María E. , Parra-Dominguez Javier , Omatu Sigeru , Herrera-Viedma Enrique , Corchado Juan Manuel

Identyfikatory

DOI

10.2478/jaiscr-2022-0006

• Języki publikacji: EN

Abstrakty

EN

Machine Learning (ML) is a disruptive concept that has given rise to and generated interest in different applications in many fields of study. The purpose of Machine Learning is to solve real-life problems by automatically learning and improving from experience without being explicitly programmed for a specific problem, but for a generic type of problem. This article approaches the different applications of ML in a series of econometric methods. Objective: The objective of this research is to identify the latest applications and do a comparative study of the performance of econometric and ML models. The study aimed to find empirical evidence for the performance of ML algorithms being superior to traditional econometric models. The Methodology of systematic mapping of literature has been followed to carry out this research, according to the guidelines established by [39], and [58] that facilitate the identification of studies published about this subject. Results: The results show, that in most cases ML outperforms econometric models, while in other cases the best performance has been achieved by combining traditional methods and ML applications. Conclusion: inclusion and exclusions criteria have been applied and 52 articles closely related articles have been reviewed. The conclusion drawn from this research is that it is a field that is growing, which is something that is well known nowadays and that there is no certainty as to the performance of ML being always superior to that of econometric models.

Słowa kluczowe

EN

machine learning; econometric models; regression; prediction

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2022
• Tom: Vol. 12, No. 2
• Strony: 79--100
• Opis fizyczny: Bibliogr. 76 poz., rys.

Twórcy

autor

Pérez-Pons María E.

• University of Salamanca, BISITE Research Group Edificio I+D+i, Calle Espejo 2, 37007, Salamanca, Spain

autor

Parra-Dominguez Javier

• University of Salamanca, BISITE Research Group Edificio I+D+i, Calle Espejo 2, 37007, Salamanca, Spain
• Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain

autor

Omatu Sigeru

• Hiroshima University, Digital Manufacturing Education and Research Center Division of Data Driven Smart System 3-10-31 Kagamiyama East-Hiroshima, 739-0046, Japan

autor

Herrera-Viedma Enrique

• University of Granada Colegio Máximo de Cartuja, Campus Universitario de Cartuja C.P. 18071 Granada, Spain

autor

Corchado Juan Manuel

• University of Salamanca, BISITE Research Group Edificio I+D+i, Calle Espejo 2, 37007, Salamanca, Spain
• Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
• Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, 535-8585 Osaka, Japan
• Pusat Komputeran dan Informatik, Universiti Malaysia Kelantan, Karung Berkunci 36, Pengkaan Chepa, 16100 Kota Bharu, Kelantan, Malaysia.

Bibliografia

• [1] Nesreen K Ahmed, Amir F Atiya, Neamat El Gayar, and Hisham El-Shishiny. An empirical comparison of machine learning models for time series forecasting. Econometric Reviews, 29(5-6): 594–621, 2010.
• [2] Andres Arevalo, Jaime Nino, Diego Leon, German Hernandez, and Javier Sandoval. Deep learning and wavelets for high-frequency price forecasting. In International Conference on Computational Science, pages 385–399. Springer, 2018.
• [3] Susan Athey. The impact of machine learning on economics. In The economics of artificial intelligence: An agenda, pages 507–547. University of Chicago Press, 2018.
• [4] Susan Athey and Guido W Imbens. Machine learning methods that economists should know about. Annual Review of Economics, 11: 685–725, 2019.
• [5] Fouad Bahrpeyma, Mark Roantree, and Andrew McCarren. Multistep-ahead prediction: A comparison of analytical and algorithmic approaches. In International Conference on Big Data Analytics and Knowledge Discovery, pages 345–354. Springer, 2018.
• [6] Patrick Bajari, Denis Nekipelov, Stephen P Ryan, and Miaoyu Yang. Machine learning methods for demand estimation. American Economic Review, 105(5): 481–85, 2015.
• [7] Alexandre Belloni, Daniel Chen, Victor Chernozhukov, and Christian Hansen. Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica, 80(6): 2369–2429, 2012.
• [8] Julien Boelaert and Etienne Ollion. The great regression. Revue francaise de sociologie, 59(3): 475–506, 2018.
• [9] Stefano Bonini and Giuliana Caivano. Probability of default modeling: A machine learning approach. In Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 173–177. Springer, 2018.
• [10] Wichor M Bramer, Melissa L Rethlefsen, Jos Kleijnen, and Oscar H Franco. Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study. Systematic reviews, 6(1): 1–12, 2017.
• [11] Vincenzo Buttice, Carlotta Orsenigo, and Mike Wright. The effect of information asymmetries on serial crowdfunding and campaign success. Economia e Politica Industriale, 45(2): 143–173, 2018.
• [12] Rich Caruana and Alexandru Niculescu-Mizil. An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd international conference on Machine learning, pages 161–168, 2006.
• [13] Oguzhan Cepni, I Ethem Guney, and Norman R. Swanson. Nowcasting and forecasting gdp in emerging markets using global financial and macroeconomic diffusion indexes. International Journal of Forecasting, 35(2): 555–572, 2019.
• [14] Manojit Chattopadhyay and Subrata Kumar Mitra. Comparative decision models for anticipating shortage of food grain production in india. Theoretical and applied climatology, 131(1-2): 523–530, 2018.
• [15] Kanchana Chokethaworn, Chukiat Chaiboonsri, and Satawat Wannapan. Alternative prediction methods in the stock exchanges of thailand. In Journal of Physics: Conference Series, volume 1324, page 012086. IOP Publishing, 2019.
• [16] Ray W Cooksey. Illustrating statistical procedures: Finding meaning in quantitative data. Springer Nature, 2014.
• [17] Huy Duc Dang, Au Hai Thi Dam, Thuyen Thi Pham, and Tra My Thi Nguyen. Determinants of credit demand of farmers in lam dong, vietnam. Agricultural Finance Review, 2019.
• [18] Jacopo De Stefani, Olivier Caelen, Dalila Hattab, Yann-Ael Le Borgne, and Gianluca Bontempi. A ¨multivariate and multi-step ahead machine learning approach to traditional and cryptocurrencies volatility forecasting. In ECML PKDD 2018 Workshops, pages 7–22. Springer, 2018.
• [19] Jacopo De Stefani, Yann-Ael Le Borgne, Olivier Caelen, Dalila Hattab, and Gianluca Bontempi.Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting. International Journal of Data Science and Analytics, 7(4): 311–329, 2019.
• [20] Mette Brandt Eriksen and Tove Faber Frandsen. The impact of patient, intervention, comparison, outcome (pico) as a search strategy tool on literature search quality: a systematic review. Journal of the Medical Library Association: JMLA, 106(4): 420, 2018.
• [21] Jan Alexander Fischer, Philipp Pohl, and Dietmar Ratz. A machine learning approach to univariate time series forecasting of quarterly earnings. Review of Quantitative Finance and Accounting, pages 1–17, 2020.
• [22] Raffaella Folgieri, Tea Baldigara, and Maja Mamula. Artificial neural networks-based econometric models for tourism demand forecasting. Tourism in South East Europe..., 4: 169–182, 2017.
• [23] Jason Furman and Robert Seamans. Ai and the economy. Innovation Policy and the Economy, 19(1): 161–191, 2019.
• [24] Marcio GP Garcia, Marcelo C Medeiros, and Gabriel FR Vasconcelos. Real-time inflation forecasting with high-dimensional models: The case of brazil. International Journal of Forecasting, 33(3): 679–693, 2017.
• [25] Jose Ignacio Gimenez-Nadal, Miguel Lafuente, Jose Alberto Molina, and Jorge Velilla. Resampling and bootstrap algorithforecastingms to assess the relevance of variables: applications to cross section entrepreneurship data. Empirical Economics, 56(1): 233–267, 2019.
• [26] Chloe Kim Glaeser, Marshall Fisher, and Xuanming Su. Optimal retail location: Empirical methodology and application to practice: Finalist–2017 m&som practice-based research competition. Manufacturing & Service Operations Management, 21(1): 86–102, 2019.
• [27] Periklis Gogas, Theophilos Papadimitriou, Vasilios Plakandaras, and Rangan Gupta. The informational content of the term-spread in forecasting the us inflation rate: A nonlinear approach. Available at SSRN 2990336, 2017.
• [28] Christian Gourieroux and Alain Monfort. Statistics and econometric models, volume 1. Cambridge University Press, 1995.
• [29] Coskun Hamzacebi, Huseyin Avni Es, and Recep Cakmak. Forecasting of turkey’s monthly electricity demand by seasonal artificial neural network. Neural Computing and Applications, pages 1–15,2019.
• [30] Nikolas Herbst, Ayman Amin, Artur Andrzejak, Lars Grunske, Samuel Kounev, Ole J Mengshoel, and Priya Sundararajan. Online workload forecasting. In Self-Aware Computing Systems, pages 529–553. Springer, 2017.
• [31] Gabriel Paes Herrera, Michel Constantino, Benjamin Miranda Tabak, Hemerson Pistori, Jen-JeSu, and Athula Naranpanawa. Long-term forecast of energy commodities price using machine learning. Energy, 179: 214–221, 2019.
• [32] Ming-Wei Hsu, Stefan Lessmann, Ming-Chien Sung, Tiejun Ma, and Johnnie EV Johnson. Bridging the divide in financial market forecasting: machine learners vs. financial economists. Expert Systems with Applications, 61: 215–234, 2016.
• [33] Jennifer Ifft, Ryan Kuhns, and Kevin Patrick. Can machine learning improve prediction–an application with farm survey data. International Food and Agribusiness Management Review, 21(1030-2019-611): 1083–1098, 2018.
• [34] Christoph Jahnz. An introduction to the nmpcgraph as general schema for causal modeling of nonlinear, multivariate, dynamic, and recursive systems with focus on time-series prediction. In Proceedings of SAI Intelligent Systems Conference, pages 825–852. Springer, 2016.
• [35] H Jang and J Lee. Machine learning versus econometric jump models in predictability and domain adaptability of index options. Physica A: Statistical Mechanics and its Applications, 513: 74–86, 2019.
• [36] Sudan Jha, Eunmok Yang, Alaa Omran Almagrabi, Ali Kashif Bashir, and Gyanendra Prasad Joshi. Comparative analysis of time series model and machine testing systems for crime forecasting. NEURAL COMPUTING & APPLICATIONS, 2020.
• [37] George Judge. Some comments on the current state of econometrics. Annual Review of Resource Economics, 8: 1–6, 2016.
• [38] Jan Kalina and Jaroslav Hlinka. On coupling robust estimation with regularization for highdimensional data. In Data Science, pages 15–27. Springer, 2017.
• [39] Barbara Kitchenham and Pearl Brereton. A systematic review of systematic review process research in software engineering. Information and software technology, 55(12): 2049–2075, 2013.
• [40] Barbara Kitchenham and Stuart Charters. Guidelines for performing systematic literature reviews in software engineering. Citeseer, 2007.
• [41] Barbara A Kitchenham, David Budgen, and O Pearl Brereton. Using mapping studies as the basis for further research–a participant-observer case study. Information and Software Technology, 53(6): 638–651, 2011.
• [42] Sotiris B Kotsiantis, I Zaharakis, and P Pintelas. Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160(1): 3–24, 2007.
• [43] Vladik Kreinovich, Nguyen Ngoc Thach, Nguyen Duc Trung, and Dang Van Thanh. Beyond Traditional Probabilistic Methods in Economics, volume 809. Springer, 2018.
• [44] Yan Liu and Tian Xie. Machine learning versus econometrics: prediction of box office. Applied Economics Letters, 26(2): 124–130, 2019.
• [45] Marcos Lopez de Prado. Beyond econometrics: A roadmap towards financial machine learning. Available at SSRN 3365282, 2019.
• [46] Sheng-Xiang Lv, Lu Peng, and Lin Wang. Stacked autoencoder with echo-state regression for tourism demand forecasting using search query data. Applied Soft Computing, 73: 119–133, 2018.
• [47] Dusan Marcek. Statistical models and granular soft rbf neural network for malaysia klci price index prediction. In International Work-Conference on Time Series Analysis, pages 401–412. Springer, 2016.
• [48] Sendhil Mullainathan and Jann Spiess. Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2): 87–106, 2017.
• [49] P Murali, R Revathy, S Balamurali, and AS Tayade. Integration of rnn with garch refined by whale optimization algorithm for yield forecasting: a hybrid machine learning approach. JOURNAL OF
• [50] Hung T Nguyen, Nguyen Duc Trung, and Nguyen Ngoc Thach. Beyond traditional probabilistic methods in econometrics. In International Econometric Conference of Vietnam, pages 3–21. Springer, 2019.
• [51] Isaac Odun-Ayo, Olasupo Ajayi, Rowland GoddyWorlu, and Jamaiah Yahaya. A systematic mapping study of cloud resources management and scalability in brokering, scheduling, capacity planning and elasticity. Asian Journal of Scientific Research, 2019.
• [52] Sharyn O’Halloran, Marion Dumas, Sameer Maskey, Geraldine McAllister, and David K Park. Computational data sciences and the regulation of banking and financial services. In From Social Data Mining and Analysis to Prediction and Community Detection, pages 179–209. Springer, 2017.
• [53] Timothy Oladunni and Sharad Sharma. Hedonic housing theory—a machine learning investigation. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 522–527. IEEE, 2016.
• [54] Meryem Ouahilal, Mohammed El Mohajir, Mohamed Chahhou, and Badr Eddine El Mohajir. A novel hybrid model based on hodrick–prescott filter and support vector regression algorithm for optimizing stock market price prediction. Journal of Big Data, 4(1): 31, 2017.
• [55] Miguel Paredes. A case study on reducing auto insurance attrition with econometrics, machine learning, and a/b testing. In 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pages 410–414. IEEE, 2018.
• [56] Jose Francisco Perles-Ribes, Ana Belen Ramon- Rodrıguez, Luis Moreno-Izquierdo, and Martın Sevilla-Jimenez. Economic crises and market performance—a machine learning approach. Tourism Economics, 23(3): 692–696, 2017.
• [57] Kai Petersen, Robert Feldt, Shahid Mujtaba, and Michael Mattsson. Systematic mapping studies in software engineering. In 12th International Conference on Evaluation and Assessment in Software Engineering (EASE) 12, pages 1–10, 2008.
• [58] Kai Petersen, Sairam Vakkalanka, and Ludwik Kuzniarz. Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64: 1–18, 2015.
• [59] Mark Petticrew and Helen Roberts. Systematic reviews in the social sciences: A practical guide. John Wiley & Sons, 2008.
• [60] Vasilios Plakandaras, Theophilos Papadimitriou, and Periklis Gogas. Forecasting transportation demand for the us market. Transportation Research Part A: Policy and Practice, 126: 195–214, 2019.
• [61] Ignacio Rojas, Hector Pomares, and Olga Valenzuela. Advances in Time Series Analysis and Forecasting: Selected Contributions from ITISE 2016. pringer, 2017.
• [62] Foued Saadaoui and Hana Rabbouch. A wavelet- based hybrid neural network for short-term electricity prices forecasting. Artificial Intelligence Review, 52(1): 649–669, 2019.
• [63] Laurie A Schintler. Regional policy analysis in the era of spatial big data. In Development Studies in Regional Science, pages 93–109. Springer, 2020.
• [64] Indranil SenGupta, William Nganje, and Erik Hanson. Refinements of barndorff-nielsen and shephard model: an analysis of crude oil price with machine learning. Annals of Data Science, pages 1–17, 2019.
• [65] Indranil SenGupta, William Nganje, and Erik Hanson. Refinements of barndorff-nielsen and shephard model: an analysis of crude oil price with machine learning. Annals of Data Science, pages 1–17, 2020.
• [66] Avald Sommervoll and Dag Einar Sommervoll. Learning from man or machine: Spatial fixed effects in urban econometrics. Regional Science and Urban Economics, 77: 239–252, 2019.
• [67] Standford, Index 2018, https://hai.stanford.edu/ai -index-2018, 2018.
• [68] Standford, Index 2019, https://hai.stanford.edu/research/ai-index -2019, 2019.
• [69] Falco J Bargagli Stoffi and Giorgio Gnecco. Causal tree with instrumental variable: an extension of the causal tree framework to irregular assignment mechanisms. International Journal of Data Science and Analytics, pages 1–23, 2019.
• [70] Falco J Bargagli Stoffi and Giorgio Gnecco. Causal tree with instrumental variable: an extension of the causal tree framework to irregular assignment mechanisms. International Journal of Data Science and Analytics, 9(3): 315–337, 2020.
• [71] Jan Tinbergen. Shaping the world economy; suggestions for an international economic policy. NA,1962.
• [72] Agostino Valier. Who performs better? avms vs hedonic models. Journal of Property Investment & Finance, 2020.
• [73] Claus Weihs and Katja Ickstadt. Data science: the impact of statistics. International Journal of Data Science and Analytics, 6(3): 189–194, 2018.
• [74] Claes Wohlin, Per Runeson, Paulo Anselmo da Mota Silveira Neto, Emelie Engstrom, Ivan do Carmo Machado, and Eduardo Santana De Almeida. On the reliability of mapping studies in software engineering. Journal of Systems and Software, 86(10): 2594–2610, 2013.
• [75] Yong Yoon. Spatial choice modeling using the support vector machine (svm): Characterization and prediction. In International Conference of the Thailand Econometrics Society, pages 767–778.Springer, 2018.
• [76] Xin Zhang, Tianyuan Xue, and H Eugene Stanley. Comparison of econometric models and artificial neural networks algorithms for the prediction of baltic dry index. IEEE Access, 7: 1647–1657, 2018.