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

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Cook, D. (2017), Practical Machine Learning with H2O:Powerful, Scalable Techniques for Deep Learning and AI, Sebastopol, CA: O’Reilly Media. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Fazekas, K. (2000), The Impact of Foreign Direct Investment Inflows on Regional Labour Market in Hungary, SOCO Project Paper, no. 77c. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Guyon, I. & Elisseeff, A. (2003), ‘An introduction to variable and feature selection Isabelle,’ Journal of Machine Learning Research, vol. 3, pp. 1157–1182. Search in Google Scholar

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] Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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] Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar