Visa trial of international trade: evidence from support vector machines and neural networks

References

• Akman, E. (2016). The facilitating role of visa policies on international trade and foreign direct investment. Turkish Studies, 17(4), 712–732. doi: 10.1080/14683849.2016.1232589
   Web of Science ®Google Scholar
• Baltagi, B. H., Egger, P., & Pfaffermayr, M. (2003). A generalized design for bilateral trade flow models. Economics Letters, 80(3), 391–397. doi: 10.1016/S0165-1765(03)00115-0
   Web of Science ®Google Scholar
• Batchelor, R., & Dua, P. (1995). Forecaster diversity and the benefits of combining forecasts. Management Science, 41(1), 68–75. doi: 10.1287/mnsc.41.1.68
   Web of Science ®Google Scholar
• Berthelon, M., & Freund, C. (2008). On the conservation of distance in international trade. Journal of International Economics, 75(2), 310–320. doi: 10.1016/j.jinteco.2007.12.005
   Web of Science ®Google Scholar
• Boubacar, I. (2016). Spatial determinants of US FDI and exports in OECD countries. Economic Systems, 40(1), 135–144. doi: 10.1016/j.ecosys.2015.04.005
   Web of Science ®Google Scholar
• Chen, A. S., & Leung, M. T. (2004). Regression neural network for error correction in foreign exchange forecasting and trading. Computers & Operations Research, 31(7), 1049–1068. doi: 10.1016/S0305-0548(03)00064-9
   Web of Science ®Google Scholar
• Chen, K. Y., & Wang, C. H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management, 28(1), 215–226. doi: 10.1016/j.tourman.2005.12.018
   Web of Science ®Google Scholar
• Chi-Hsien, K., & Nagasawa, S. (2019). Applying machine learning to market analysis: Knowing your luxury consumer. Journal of Management Analytics, 6(4), 404–419.
   Web of Science ®Google Scholar
• Ching, S., Wong, C. Y. P., & Zhang, A. (2004). Non-tariff barriers to trade in the pacific rim. Pacific Economic Review, 9(1), 65–73. doi: 10.1111/j.1468-0106.2004.00237.x
   Google Scholar
• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Oxfordshire: Routledge.
   Google Scholar
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. doi: 10.1007/BF00994018
   Web of Science ®Google Scholar
• Cui, D., & Curry, D. (2005). Prediction in marketing using the support vector machine. Marketing Science, 24(4), 595–615. doi: 10.1287/mksc.1050.0123
   Web of Science ®Google Scholar
• Czaika, M., & Neumayer, E. (2017). Visa restrictions and economic globalisation. Applied Geography, 84, 75–82. doi: 10.1016/j.apgeog.2017.04.011
   Web of Science ®Google Scholar
• Davis, G. W. (1989). Sensitivity analysis in neural net solutions. IEEE Transactions on Systems, Man, and Cybernetics, 19(5), 1078–1082. doi: 10.1109/21.44023
   Web of Science ®Google Scholar
• Delen, D., Kuzey, C., & Uyar, A. (2013). Measuring firm performance using financial ratios: A decision tree approach. Expert Systems with Applications, 40(10), 3970–3983. doi: 10.1016/j.eswa.2013.01.012
   Web of Science ®Google Scholar
• Delen, D., Oztekin, A., & Tomak, L. (2012). An analytic approach to better understanding and management of coronary surgeries. Decision Support Systems, 52(3), 698–705. doi: 10.1016/j.dss.2011.11.004
   Web of Science ®Google Scholar
• Delen, D., Zaim, H., Kuzey, C., & Zaim, S. (2013). A comparative analysis of machine learning systems for measuring the impact of knowledge management practices. Decision Support Systems, 54(2), 1150–1160. doi: 10.1016/j.dss.2012.10.040
   Web of Science ®Google Scholar
• Disdier, A. C., & Head, K. (2008). The puzzling persistence of the distance effect on bilateral trade. The Review of Economics and Statistics, 90(1), 37–48. doi: 10.1162/rest.90.1.37
   Web of Science ®Google Scholar
• Dunis, C. L., Laws, J., & Sermpinis, G. (2010). Modelling and trading the EUR/USD exchange rate at the ECB fixing. The European Journal of Finance, 16(6), 541–560. doi: 10.1080/13518470903037771
   Web of Science ®Google Scholar
• Fuller, C. M., Biros, D. P., & Delen, D. (2011). An investigation of data and text mining methods for real world deception detection. Expert Systems with Applications, 38(7), 8392–8398. doi: 10.1016/j.eswa.2011.01.032
   Web of Science ®Google Scholar
• Huang, C. L., Chen, M. C., & Wang, C. J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications, 33(4), 847–856. doi: 10.1016/j.eswa.2006.07.007
   Web of Science ®Google Scholar
• Huang, W., Nakamori, Y., & Wang, S. Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513–2522. doi: 10.1016/j.cor.2004.03.016
   Web of Science ®Google Scholar
• Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37(4), 543–558. doi: 10.1016/S0167-9236(03)00086-1
   Web of Science ®Google Scholar
• Karaman, A. S. (2016). The pernicious impact of visa restrictions on inbound tourism: The case of Turkey. Turkish Studies, 17(3), 502–524. doi: 10.1080/14683849.2016.1170602
   Web of Science ®Google Scholar
• Kim, D. H., Kim, T. J., Wang, X., Kim, M., Quan, Y. J., Oh, J. W., … Ahn, S. H. (2018). Smart machining process using machine learning: A review and perspective on machining industry. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(4), 555–568.
   Web of Science ®Google Scholar
• Kim, J. H. (2017). A review of cyber-physical system research relevant to the emerging IT trends: Industry 4.0. IoT, big data, and cloud computing. Journal of Industrial Integration and Management, 2(03), 1750011.
   Google Scholar
• Kulendran, N., & Wilson, K. (2000). Is there a relationship between international trade and international travel? Applied Economics, 32(8), 1001–1009. doi: 10.1080/000368400322057
   Web of Science ®Google Scholar
• Kuzey, C. (2018). Impact of health care employees job satisfaction on organizational performance support vector machine approach. Journal of Economics and Financial Analysis, 2(1), 45–68.
   Google Scholar
• Kuzey, C., Karaman, A. S., & Akman, E. (2019). Elucidating the impact of visa regimes: A decision tree analysis. Tourism Management Perspectives, 29, 148–156. doi: 10.1016/j.tmp.2018.11.008
   Web of Science ®Google Scholar
• Lam, M. (2004). Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis. Decision Support Systems, 37(4), 567–581. doi: 10.1016/S0167-9236(03)00088-5
   Web of Science ®Google Scholar
• Li, H. X., & Da, X. L. (2000). A neural network representation of linear programming. European Journal of Operational Research, 124(2), 224–234.
   Web of Science ®Google Scholar
• Min, J. H., & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603–614. doi: 10.1016/j.eswa.2004.12.008
   Web of Science ®Google Scholar
• Modeler, I. S. (2015). Algorithms guide. New York: IBM Corporation.
   Google Scholar
• Mohelska, H., & Sokolova, M. (2018). Management approaches for Industry 4.0 – the organizational culture perspective. Technological and Economic Development of Economy, 24(6), 2225–2240. doi: 10.3846/tede.2018.6397
   Web of Science ®Google Scholar
• Niu, W. J., Feng, Z. K., Feng, B. F., Min, Y. W., Cheng, C. T., & Zhou, J. Z. (2019). Comparison of multiple linear regression, artificial neural network, extreme learning machine, and support vector machine in deriving operation rule of hydropower reservoir. Water, 11(1), 88.
   Web of Science ®Google Scholar
• Olson, D. L., Delen, D., & Meng, Y. (2012). Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, 52(2), 464–473. doi: 10.1016/j.dss.2011.10.007
   Web of Science ®Google Scholar
• Olson, D., & Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19(3), 453–465. doi: 10.1016/S0169-2070(02)00058-4
   Web of Science ®Google Scholar
• Oxford Economics. (2011). Business travel: A catalyst for economic performance. Oxford: WTTC (World Travel & Tourism Council).
   Google Scholar
• Panigrahi, B. K., Nath, T. K., & Senapati, M. R. (2019). An application of local linear radial basis function neural network for flood prediction. Journal of Management Analytics, 6(1), 67–87.
   Web of Science ®Google Scholar
• Ramakalyan, A., Sivakumar, A., Aravindan, C., Kannan, K., Swaminathan, V., & Sarala, D. (2016). Development of KSVGRNN: A hybrid soft computing technique for estimation of boiler flue gas components. Journal of Industrial Information Integration, 4, 42–51.
   Web of Science ®Google Scholar
• Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity analysis in practice: A guide to assessing scientific models. New York: John Wiley & Sons.
   Google Scholar
• Sauro, J., & Lewis, J. R. (2009 April). Correlations among prototypical usability metrics: Evidence for the construct of usability. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1609–1618). ACM. doi: 10.1145/1518701.1518947
   Google Scholar
• Seni, G., & Elder, J. (2010). Ensemble methods in data mining: Improving accuracy through combining predictions. Williston, VT: Morgan & Claypool Publishers.
   Google Scholar
• Shan, J., & Wilson, K. (2001). Causality between trade and tourism: Empirical evidence from China. Applied Economics Letters, 8(4), 279–283. doi: 10.1080/135048501750104114
   Web of Science ®Google Scholar
• Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5(4), 13–22.
   Google Scholar
• Shen, L., Wang, H., Da Xu, L., Ma, X., Chaudhry, S., & He, W. (2016). Identity management based on PCA and SVM. Information Systems Frontiers, 18(4), 711–716.
   Web of Science ®Google Scholar
• Shin, K. S., Lee, T. S., & Kim, H. J. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), 127–135. doi: 10.1016/j.eswa.2004.08.009
   Web of Science ®Google Scholar
• Song, H., Gartner, W. C., & Tasci, A. D. (2012). Visa restrictions and their adverse economic and marketing implications – evidence from China. Tourism Management, 33(2), 397–412. doi: 10.1016/j.tourman.2011.05.001
   Web of Science ®Google Scholar
• Starr, A., & Desforges, M. (1998). Strategies in Data Fusion-Sorting Through the Tool Box. Bedworth and O’Brien [BO98], 85–90.
   Google Scholar
• Tay, F. E., & Cao, L. J. (2002). Modified support vector machines in financial time series forecasting. Neurocomputing, 48(1–4), 847–861. doi: 10.1016/S0925-2312(01)00676-2
   Google Scholar
• Tsai, C. F., & Wu, J. W. (2008). Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications, 34(4), 2639–2649. doi: 10.1016/j.eswa.2007.05.019
   Web of Science ®Google Scholar
• Tsui, W. H. K., Balli, F., Tan, D. T. W., Lau, O., & Hasan, M. (2018). New Zealand business tourism: Exploring the impact of economic policy uncertainties. Tourism Economics, 24(4), 386–417. doi: 10.1177/1354816617731387
   Web of Science ®Google Scholar
• Tsui, W. H. K., & Fung, M. K. Y. (2016). Causality between business travel and trade volumes: Empirical evidence from Hong Kong. Tourism Management, 52, 395–404. doi: 10.1016/j.tourman.2015.07.010
   Web of Science ®Google Scholar
• Vafeiadis, T., Dimitriou, N., Ioannidis, D., Wotherspoon, T., Tinker, G., & Tzovaras, D. (2018). A framework for inspection of dies attachment on PCB utilizing machine learning techniques. Journal of Management Analytics, 5(2), 81–94.
   Web of Science ®Google Scholar
• Van De Vijver, E., Derudder, B., & Witlox, F. (2014). Exploring causality in trade and air passenger travel relationships: The case of Asia-Pacific, 1980–2010. Journal of Transport Geography, 34, 142–150. doi: 10.1016/j.jtrangeo.2013.12.001
   Web of Science ®Google Scholar
• Vapnik, V. (1995). The nature of statistical learning theory. New York, NY: Springer-Verlag.
   Google Scholar
• West, D., Dellana, S., & Qian, J. (2005). Neural network ensemble strategies for financial decision applications. Computers & Operations Research, 32(10), 2543–2559. doi: 10.1016/j.cor.2004.03.017
   Web of Science ®Google Scholar
• World Development Indicators (WDI) Database. (2018 April). Retrieved from https://data.worldbank.org/
   Google Scholar
• World Trade Organization. (2018). World trade statistical review 2018. Geneva, Switzerland: Author.
   Google Scholar
• Xu, L. D., & Duan, L. (2019). Big data for cyber physical systems in industry 4.0: A survey. Enterprise Information Systems, 13(2), 148–169.
   Web of Science ®Google Scholar
• Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941–2962.
   Web of Science ®Google Scholar
• Yakan, B. (2015). Uluslararası Seyahat ve Vize Anlaşmaları [International travel and visa agreements]. Ankara: Nobel Publications.
   Google Scholar
• Yasar, M., Lisner, D., & Rejesus, R. M. (2012). Bilateral trade impacts of temporary foreign visitor policy. Review of World Economics, 148(3), 501–521. doi: 10.1007/s10290-012-0122-5
   Web of Science ®Google Scholar
• Yazdani, M., Babagolzadeh, M., Kazemitash, N., & Saberi, M. (2019). Reliability estimation using an integrated support vector regression–variable neighborhood search model. Journal of Industrial Information Integration, 15, 103–110.
   PubMed Web of Science ®Google Scholar
• Yli-Ojanperä, M., Sierla, S., Papakonstantinou, N., & Vyatkin, V. (2019). Adapting an agile manufacturing concept to the reference architecture model Industry 4.0: A survey and case study. Journal of Industrial Information Integration, 15, 147–160.
   Web of Science ®Google Scholar
• Yu, L., Yue, W., Wang, S., & Lai, K. K. (2010). Support vector machine based multiagent ensemble learning for credit risk evaluation. Expert Systems with Applications, 37(2), 1351–1360. doi: 10.1016/j.eswa.2009.06.083
   Web of Science ®Google Scholar
• Yuan, R., Li, Z., Guan, X., & Xu, L. (2010). An SVM-based machine learning method for accurate internet traffic classification. Information Systems Frontiers, 12(2), 149–156.
   Web of Science ®Google Scholar
• Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. doi: 10.1016/S0925-2312(01)00702-0
   Web of Science ®Google Scholar
• Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160(2), 501–514. doi: 10.1016/j.ejor.2003.08.037
   Web of Science ®Google Scholar
• Zhou, S. M., & Da Xu, L. (2001). A new type of recurrent fuzzy neural network for modeling dynamic systems. Knowledge-Based Systems, 14(5–6), 243–251.
   Web of Science ®Google Scholar