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Interpretable Machine-Learning Approach in Estimating FDI Inflow: Visualization of ML Models with LIME and H2O Cover

Interpretable Machine-Learning Approach in Estimating FDI Inflow: Visualization of ML Models with LIME and H2O

By: Devesh Singh  
Open Access
|May 2021

References

  1. Abdipour, M.; Younessi-Hmazekhanlu, M.; Ramazani, S. H. R. & Omidi, A. H. (2019), ‘Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamus tinctorius L.),’ Industrial Crops and Products, vol. 127(November 2018), pp. 185–194. https://doi.org/10.1016/j.indcrop.2018.10.05010.1016/j.indcrop.2018.10.050
  2. Akbari, A.; Ng, L. & Solnik, B. (2021), ‘Drivers of economic and financial integration : a machine learning approach,’ Journal of Empirical Finance, vol. 61, pp. 82–102. https://doi.org/10.1016/j.jempfin.2020.12.00510.1016/j.jempfin.2020.12.005
  3. Arel-Bundock, V. (2017), ‘The political determinants of foreign direct investment: a firm-level analysis,’ International Interactions, vol. 43, no. 3, pp. 424–452. https://doi.org/10.1080/03050629.2016.118501110.1080/03050629.2016.1185011
  4. Boghean, C. & State, M. (2015), ‘The relation between foreign direct investments (FDI) and labour productivity in the European Union countries,’ Procedia Economics and Finance, vol. 32, no. 15, pp. 278–285. https://doi.org/10.1016/s2212-5671(15)01392-110.1016/S2212-5671(15)01392-1
  5. Boudier-Bensebaa, F. (2005), ‘Agglomeration economies and location choice: Foreign direct investment in Hungary,’ Economics of Transition, vol. 13, no. 4, pp. 605–628. https://doi.org/10.1111/j.0967-0750.2005.00234.x10.1111/j.0967-0750.2005.00234.x
  6. Bruneckiene, J.; Jucevicius, R.; Zykiene, I.; Rapsikevicius, J. & Lukauskas, M. (2019), ‘Assessment of investment attractiveness in European countries by artificial neural networks: What competences are needed to make a decision on collective well-being?’ Sustainability, vol. 11, no. 24, art. 6892. https://doi.org/10.3390/su1124689210.3390/su11246892
  7. Chuku, C.; Simpasa, A. & Oduor, J. (2019), ‘Intelligent forecasting of economic growth for developing economies,’ International Economics, vol. 159, pp. 74–93. https://doi.org/10.1016/j.inteco.2019.06.00110.1016/j.inteco.2019.06.001
  8. Cook, D. (2017), Practical Machine Learning with H2O:Powerful, Scalable Techniques for Deep Learning and AI, Sebastopol, CA: O’Reilly Media.
  9. Das, S. & Tsapakis, I. (2020), ‘Interpretable machine learning approach in estimating traffic volume on low-volume roadways,’ International Journal of Transportation Science and Technology, vol. 9, no. 1, pp. 76–88. https://doi.org/10.1016/j.ijtst.2019.09.00410.1016/j.ijtst.2019.09.004
  10. Devereux, M. P. & Griffith, R. (2003), ‘The impact of corporate taxation on the location of capital: A review,’ Economic Analysis and Policy, vol. 33, no. 2, pp. 275–292. https://doi.org/10.1016/S0313-5926(03)50021-210.1016/S0313-5926(03)50021-2
  11. Fazekas, K. (2000), The Impact of Foreign Direct Investment Inflows on Regional Labour Market in Hungary, SOCO Project Paper, no. 77c.
  12. Fazekas, K. (2005), ‘Effects of FDI inflows on regional labour market differences in Hungary,’ Économie Internationale, vol. 102 (April 2003), pp. 83–105. https://doi.org/10.3917/ecoi.102.008310.3917/ecoi.102.0083
  13. Friedman, J. (2001), ‘Greedy boosting approximation: a gradient boosting machine,’ The Annals of Statistics, vol. 29, no. 5, pp. 1189–1232. https://doi.org/doi:10.1214/aos/101320345110.1214/aos/1013203451
  14. Gaber, M. M. & Atwal, H. S. (2013), ‘An entropy-based approach to enhancing Random Forests,’ Intelligent Decision Technologies, vol. 7, no. 4, pp. 319–327. https://doi.org/10.3233/IDT-13017110.3233/IDT-130171
  15. Gasanova, A.; Medvedev, A. N. & Komotskiy, E. I. (2017), ‘The assessment of corruption impact on the inflow of foreign direct investment,’ AIP Conference Proceedings, vol. 1836, no. 1. https://doi.org/10.1063/1.498195110.1063/1.4981951
  16. Goldstein, A.; Kapelner, A.; Bleich, J. & Pitkin, E. (2015), ‘Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation,’ Journal of Computational and Graphical Statistics, vol. 24, no. 1, pp. 44–65. https://doi.org/10.1080/10618600.2014.90709510.1080/10618600.2014.907095
  17. Guyon, I. & Elisseeff, A. (2003), ‘An introduction to variable and feature selection Isabelle,’ Journal of Machine Learning Research, vol. 3, pp. 1157–1182.
  18. Hall, P.; Gill, N.; Kurka, M.; Phan, W. & Bartz, A. (2019), Machine Learning Interpretability with H2O Driverless AI: First Edition, Mountain View, CA: H2o.ai Inc. Retrieved from http://docs.h2o.ai [accessed Mar 2021]
  19. Heravi, S.; Osborn, D. R. & Birchenhall, C. R. (2004), ‘Linear versus neural network forecasts for European industrial production series,’ International Journal of Forecasting, vol. 20, no. 3, pp. 435–446. https://doi.org/10.1016/S0169-2070(03)00062-110.1016/S0169-2070(03)00062-1
  20. Jiménez, A. & Herrero, Á. (2019), ‘Selecting features that drive internationalization of Spanish firms,’ Cybernetics and Systems, vol. 50, no. 1, pp. 25–39. https://doi.org/10.1080/01969722.2018.155801210.1080/01969722.2018.1558012
  21. Korgaonkar, C. (2012), ‘Analysis of the impact of financial development on foreign direct investment: a data mining approach,’ Journal of Economics and Sustainable Development, vol. 3, no. 6, pp. 70–79.
  22. Lengyel, I.; Vas, Z.; Kano, I. S. & Lengyel, B. (2017), ‘Spatial differences of reindustrialization in a post-socialist economy: manufacturing in the Hungarian counties,’ European Planning Studies, vol. 25, no. 8, pp. 1416–1434. https://doi.org/10.1080/09654313.2017.131946710.1080/09654313.2017.1319467
  23. Makojevic, N.; Kostic, M. & Puric, J. (2016), ‘Može li država da utiče na regionalnu distribuciju SDI—primer Češke, Mađarske, Poljske i Srbije’ [Can a state influence FDI regional distribution: the case of the Czech Republic, Hungary, Poland and Serbia], Industrija, vol. 44, no. 2, pp. 43–54. https://doi.org/10.5937/industrija44-959010.5937/industrija44-9590
  24. Molnar, C. (2019), Interpretable Machine Learning. A Guide for Making Black Box Models Explainable. Retrieved from https://christophm.github.io/interpretable-ml-book/ [accessed Mar 2021]
  25. Munday, M.; Roberts, A. & Roche, N. (2009), A Review of the Economic Evidence on the Determinants and Effects of Foreign Direct Investment, Cardiff: Cardiff Business School & Welsh Economy Research Unit.
  26. Na, L. & Lightfoot, W. S. (2006), ‘Determinants of foreign direct investment at the regional level in China,’ Journal of Technology Management in China, vol. 1, no. 3, pp. 262–278. https://doi.org/10.1108/1746877061070493010.1108/17468770610704930
  27. Natekin, A. & Knoll, A. (2013), ‘Gradient boosting machines, a tutorial,’ Frontiers in Neurorobotics, vol. 7 (Dec). https://doi.org/10.3389/fnbot.2013.0002110.3389/fnbot.2013.00021388582624409142
  28. Ozturk, I. (2001), ‘The role of education in economic development: a theoretical perspective,’ Journal of Rural Development and Administration, vol. 33, no. 1, pp. 39–47. https://doi.org/10.2139/ssrn.113754110.2139/ssrn.1137541
  29. Patra, S. (2019), ‘FDI, urbanization, and economic growth linkages in India and China,’ in Socio-Economic Development: Concepts, Methodologies, Tools, and Applications, Hershey, PA: IGI Global, pp. 313–327. https://doi.org/http://doi:10.4018/978-1-5225-7311-1.ch01710.4018/978-1-5225-7311-1.ch017
  30. Pekarskiene, I. & Susniene, R. (2015), ‘Features of foreign direct investment in the context of globalization,’ Procedia – Social and Behavioral Sciences, vol. 213, pp. 204–210. https://doi.org/10.1016/j.sbspro.2015.11.42710.1016/j.sbspro.2015.11.427
  31. Pratiwi, I. (2016), Clustered Regression Models for Analysis and Prediction of Foreign Direct Investment Inflows, MA thesis in statistics and data mining, Dept. of Computer and Information Science, Linköping University.
  32. Ribeiro, M. T.; Singh, S. & Guestrin, C. (2016), ‘“Why should i trust you?” Explaining the predictions of any classifier,’ KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17 August, pp. 1135–1144. https://doi.org/10.1145/2939672.293977810.1145/2939672.2939778
  33. Salike, N. (2016), ‘Role of human capital on regional distribution of FDI in China: new evidences,’ China Economic Review, vol. 37, pp. 66–84. https://doi.org/10.1016/j.chieco.2015.11.01310.1016/j.chieco.2015.11.013
  34. Schneider, J. (2020), Dislocations in Foreign Direct Investment: A Machine Learning Approach to Identifying Over- and Under-Invested International Markets Schneider, Second Year Policy Analysis (SYPA), Schneider Economics.
  35. Singh, D. (2021a), ‘Cluster space among labor productivity, urbanization, and agglomeration of industries in Hungary,’ Journal of the Knowledge Economy. https://doi.org/https://doi.org/10.1007/s13132-021-00726-910.1007/s13132-021-00726-9
  36. Singh, D. (2021b, forthcoming), ‘Comparison between artificial neural network and linear model prediction performance for FDI disparity and the growth rate of companies in Hungarian counties,’ International Journal of Business Information Systems. https://doi.org/10.1504/IJBIS.2020.1003450210.1504/IJBIS.2020.10034502
  37. Szántó, I. (2014), ‘Problems of a declining Hungarian birth rate: a historical perspective,’ Journal of the American Hungarian Educators Association, vol. 7, pp. 95–109. https://doi.org/10.5195/ahea.2014.110.5195/AHEA.2014.1
DOI: https://doi.org/10.2478/bjes-2021-0009 | Journal eISSN: 2674-4619 | Journal ISSN: 2674-4600
Language: English
Page range: 133 - 152
Published on: May 26, 2021
Published by: Tallinn University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2021 Devesh Singh, published by Tallinn University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.