Accès libre

Machine Learning and Regularization Technique to Determine Foreign Direct Investment in Hungarian Counties

À propos de cet article

Citez

Abadie, A., Kasy, M. (2019). Choosing among regularized estimators in empirical economics: The risk of machine learning. Review of Economics and Statistics, 101(5), 743–762. https://doi.org/10.1162/resta00812.10.1162/rest_a_00812 Search in Google Scholar

Aghion, P., F. Jones, B., I. Jones, C. (2017). Artificial intelligence and economic growth (No. 23928). Retrieved from http://www.nber.org/papers/w23928.10.3386/w23928 Search in Google Scholar

Ahrens, A., Hansen, C. B., Schaffer, M. E. (2019). Iassopack: Model Selection and Prediction with Regularized Regression in Stata. In Statistical Software Components.10.2139/ssrn.3323196 Search in Google Scholar

Chan-Lau, J. (2017). Lasso Regressions and Forecasting Models in Applied Stress Testing. IMF Working Papers, 17(108), 1.10.5089/9781475599022.001 Search in Google Scholar

Christos, T., Georgios, M. C. (2018). Comparative study of linear models and neural networks for rational decision making. IOSR Journal of Mathematics, 14(6), 46–60. Search in Google Scholar

Chuku, C., Simpasa, A., Oduor, J. (2019). Intelligent forecasting of economic growth for developing economies. International Economics, 159, 74–93. https://doi.org/10.1016/j.inteco.2019.06.001.10.1016/j.inteco.2019.06.001 Search in Google Scholar

Chulani, S., Boehm, B., Steece, B. (1999). Bayesian Analysis of Empirical Software Engineering Cost Models. Transaction of Software Engineering, 25(4), 573–583.10.1109/32.799958 Search in Google Scholar

Singh, D. (2021). Migration and Foreign Direct Investment in Hungary: a county-based panel data analysis. Macroeconomics and Finance in Emerging Market Economies, 00(00), 1–17. https://doi.org/10.1080/17520843.2021.2012967.10.1080/17520843.2021.2012967 Search in Google Scholar

Singh, D. (2022). Factors hinder the foreign entities business operation in Visegrad countries. Business Strategy and Development, January, 1–12. https://doi.org/10.1002/bsd2.198.10.1002/bsd2.198 Search in Google Scholar

Einav, L., Jonathan, D. L. (2013). The data revolution and economic analysis. In NBER working paper series (No. 19035). Retrieved from http://www.nber.org/papers/w19035.10.3386/w19035 Search in Google Scholar

Emmert-Streib, F., Dehmer, M. (2019). High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection. Machine Learning and Knowledge Extraction, 1(1), 359–383.10.3390/make1010021 Search in Google Scholar

Fan, J., Lv, J., Qi, L. (2011). Sparse high-dimensional models in economics. Annual Review of Economics, 3(September), 291–317. https://doi.org/10.1146/annurev-economics-0611-09-080451.10.1146/annurev-economics-061109-080451 Search in Google Scholar

Ghoddusi, H., Creamer, G. G., Rafizadeh, N. (2019). Machine learning in energy economics and finance: A review. Energy Economics, 81, 709–727. https://doi.org/10.1016/j.eneco.2019.05.006.10.1016/j.eneco.2019.05.006 Search in Google Scholar

Gowin, J. L., Ernst, M., Ball, T., May, A. C., Sloan, M. E., Tapert, S. F., Paulus, M. P. (2019). Using neuroimaging to predict relapse in stimulant dependence: A comparison of linear and machine learning models. NeuroImage: Clinical, 21(January), 101676. https://doi.org/10.1016/j.nicl.2019.101676.10.1016/j.nicl.2019.101676 Search in Google Scholar

Hofmarcher, P., Cuaresma, J. C., Grun, B., Hornik, K. (2011). Fishing Economic Growth Determinants Using Bayesian Elastic Nets. In Institute for Statistics and Mathematics, Vienna University of economics and businesses. Search in Google Scholar

Makojevic, N., Kostic, M., Puric, J. (2016). Can the state influence FDI regional distribution: The case of Czech Republic, Hungary, Poland and Serbia. Industrija, 44(2), 43–54. https://doi.org/10.5937/industrija44-9590.10.5937/industrija44-9590 Search in Google Scholar

McLaren, N., Shanbhogue, R. (2012). Using Internet Search Data as Economic Indicators. In SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1865276.10.2139/ssrn.1865276 Search in Google Scholar

Melkumova, L. E., Shatskikh, S. Y. (2017). Comparing Ridge and LASSO estimators for data analysis. Procedia Engineering, 201, 746–755. https://doi.org/10.1016/j.proeng.2017.09.615.10.1016/j.proeng.2017.09.615 Search in Google Scholar

Ogutu, J. O., Schulz-Streeck, T., Piepho, H. (2012). Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proceedings, 6(Suppl 2).10.1186/1753-6561-6-S2-S10 Search in Google Scholar

Pereira, J. M., Basto, M., Silva, A. F. da. (2016). The Logistic Lasso and Ridge Regression in Predicting Corporate Failure. Procedia Economics and Finance, 39(November 2015), 634–641. https://doi.org/10.1016/s2212-5671(16)30310-0.10.1016/S2212-5671(16)30310-0 Search in Google Scholar

Sabahi, S., Parast, M. M. (2020). The impact of entrepreneurship orientation on project performance: A machine learning approach. International Journal of Production Economics, 226(April 2019), 107621. https://doi.org/10.1016/j.ijpe.2020.107621.10.1016/j.ijpe.2020.107621 Search in Google Scholar

Sapra, S. (2016). Econometric Modeling with High-dimensional Data in Business and Economics. The MacroConference Proceedings, 62–69. Search in Google Scholar

Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28. https://doi.org/10.1257/jep.28.2.3.10.1257/jep.28.2.3 Search in Google Scholar

Wang, Y., Gibson, G. E. (2010). A study of preproject planning and project success using ANNs and regression models. Automation in Construction, 19(3), 341–346. https://doi.org/10.1016/j.autcon.2009.12.007.10.1016/j.autcon.2009.12.007 Search in Google Scholar

Zou, H., Hastie, T. (2005). Regularization and variable selection via the elastic net. J. R. Statist. Soc., 67(2), 301–320. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2005.00503.x/full.10.1111/j.1467-9868.2005.00503.x Search in Google Scholar

eISSN:
1804-8285
Langue:
Anglais
Périodicité:
4 fois par an
Sujets de la revue:
Affaires et économie, Économie politique, Macroéconomie, Politique économique, Droit, Droit européen, autres