기계학습을 활용한 사회과학 연구의 쟁점과 사례

키워드

참고문헌 (98)

1. 구소희, "설명중심의 통계모형과 예측중심의 기계학습의 비교", 인문사회 21, 12(2), pp.245~260 (2021), 인문사회 21
2. 김수현, "거시경제 분석을 위한 텍스트 마이닝", 한국경제의 분석, 26(1), pp.1~70 (2020), 한국경제의 분석패널[DOI]
3. 한국조세재정정책의 기회 평등화 효과에 대한 연구 : 소득획득에 대한 기회를 중심으로, "김우철", 한국조세재정연구원, ~ (2008)
4. 박기영, "머신 러닝을 이용한 경제분석", 한국경제학보(구 연세경제연구), 26(2), pp.367~408 (2019), 경제연구소
5. 박혜진, "알트메트릭스를 이용한 SSCI 등재 한국 저널의 온라인 소셜 플랫폼에서의 영향력에 관한 연구", Journal of The Korean Data Analysis Society, 20(3), pp.1427~1438 (2018), 한국자료분석학회[DOI]
6. 기계학습을 이용한 노동시장 예측모델, "방형준", 한국노동연구원, ~ (2020)
7. 유진은, "데이터 마이닝 기법을 통한 교육 패널데이터 분석: 벌점회귀모형과 KYPS 자료", 아시아교육연구, 17(3), pp.1~19 (2016), 교육연구소
8. 유진은, "기계학습: 대용량", 패널자료와 학습분석학 자료 분석으로의 활용, 2019, pp.35(2), 313~338[DOI]
9. 최호철, "정부지원 기초연구 성과의 사회적 영향력 분석: altmetrics 분석툴 적용가능성 탐색", 사회과학연구, 45(3), pp.321~344 (2019), 사회과학연구원[DOI]
10. Automated linking of historical data, "Abramitzky, R.", National Bureau of Economic Research, ~, pp.No. w25825 (2019)
11. Abramitzky, R., "Linking individuals across historical sources : A fully automated approach", Historical Methods : A Journal of Quantitative and Interdisciplinary History, 53(2), pp.94~111 (2020)
12. Achten, A., "Spatial inequality, geography and economic activity", World Development, 136, pp.105114~ (2020)[DOI]
13. Algaba, A., "Econometrics meets sentiment : An overview of methodology and applications", Journal of Economic Surveys, 34(3), pp.512~547 (2020)
14. Alpaydin, E., "Introduction to machine learning" (2020), MIT Press
15. Alsultanny, Y. A., "Labor market forecasting by using data mining", Procedia Computer Science, 18, pp.1700~1709 (2013)
16. Amarasinghe, K., "Explainable machine learning for public policy: Use cases, gaps, and research directions" (2020)[DOI]
17. Amat, C., "Fundamentals and exchange rate forecastability with simple machine learning methods", Journal of International Money and Finance, 88, pp.1~24 (2018)
18. Angrist, J., "Economic research evolves : Fields and styles", American Economic Review, 107(5), pp.293~297 (2017)
19. Angrist, J., "Inside job or deep impact? Extramural citations and the influence of economic scholarship", Journal of Economic Literature, 58(1), pp.3~52 (2020)
20. Google econometrics and unemployment forecasting, "Askitas, N.", IZA DP, ~, pp.discussion paper No. 4201 (2009)
21. Athey, S., "Machine learning and causal inference for policy evaluation", Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp.5~6 (2015)
22. Athey, S., "Beyond prediction : Using big data for policy problems", Science, 355(6324), pp.483~485 (2017)
23. Athey, S, "The economics of artificial intelligence: An agenda", pp.507~547 (2018), University of Chicago Press
24. Athey, S., "The economics of artificial intelligence: An agenda", pp.507~547 (2019), University of Chicago Press
25. Athey, S., "Machine learning methods for estimating heterogeneous causal effects", Stat, 1050(5), pp.1~26 (2015)
26. Athey, S., "The state of applied econometrics-causality and policy evaluation", Journal of Economic Perspectives, 31(2), pp.3~32 (2016)
27. Athey, S., "Machine learning methods that economists should know about", Annual Review of Economics, 11, pp.685~725 (2019)
28. Bailey, M. J., "How well do automated linking methods perform? Lessons from US historical data", Journal of Economic Literature, 58(4), pp.997~1044 (2020)
29. Bajari, P., "Machine learning methods for demand estimation", American Economic Review, 105(5), pp.481~485 (2015)
30. Ballestar, M. T., "A novel machine learning approach for evaluation of public policies : An application in relation to the performance of university researchers", Technological Forecasting and Social Change, 149, pp.119756~ (2019)
31. Forecasting with economic news, "Barbaglia, L.", ~, pp.SSRN working paper 3698121 (2021)
32. Beyer, R. C., "Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity", World Development, 140, pp.105287~ (2021)
33. Björklund, , A., "Intergenerational top income mobility in Sweden: Capitalist dynasties in the land of equal opportunity?", Journal of Public Economics, 96(5-6), pp.474~484 (2012)
34. Blaney, J., "Doing digital history:A beginner’s guide to working with text as data" (2021), Manchester University Press
35. Blei, D. M., "Latent dirichlet allocation", Journal of machine Learning research, 3, pp.993~1022 (2003)
36. Blundell, J., "Income and family background: Are we using the right models?" (2019)[DOI]
37. Borup, D., "In search of a job : Forecasting employment growth using Google Trends", Journal of Business &Economic Statistics, 40(1), pp.186~200 (2022)
38. Borup, D., "Mixed-frequency machine learning : Nowcasting and backcasting weekly initial claims with daily internet search volume data", International Journal of Forecasting (2022)[DOI]
39. "A new paradigm for the introductory course in economics" (2017)[URL]
40. Bowles, S., "What students learn in economics 101 : Time for a change", Journal of Economic Literature, 58(1), pp.176~214 (2020)
41. Bruederle, A., "Nighttime lights as a proxy for human development at the local level", PloS one, 13(9), pp.e0202231~ (2018)
42. The evolution of inequality of opportunity in Germany: A machine learning approach, "Brunori, P.", ZEW-Centre for European Economic Research, ~, pp.ZEW-Centre for European Economic Research Discussion Paper 20-013 (2020)
43. "The roots of inequality:Estimating inequality of opportunity from regression trees and forests" (2019)[URL]
44. Brunori, P., "Upward and downward bias when measuring inequality of opportunity", Social Choice and Welfare, 52(4), pp.635~661 (2019)
45. Using Satellite Imagery to Understand and Promote Sustainable Development, "Burke, M.", NBER, ~, pp.NBER w27879 (2020)
46. Business news and business cycles, "Bybee, L.", NBER, ~, pp.NBER w29344 (2021)
47. Card, D., "Myth and measurement: The new economics of the minimum wage" (1995), Princeton University Press
48. Seeing beyond the trees: Using machine learning to estimate the impact of minimum wages on labor market outcomes, "Cengiz, D.", NBER, ~, pp.NBER w28399 (2021)
49. Ch, R., "Measuring the size and growth of cities using nighttime light", Journal of Urban Economics, 125, pp.103254~ (2021)[DOI]
50. Chalfin, A., "Productivity and selection of human capital with machine learning", American Economic Review, 106(5), pp.124~127 (2016)
51. Checchi, D., "Inequality of opportunity in Italy", The Journal of Economic Inequality, 8(4), pp.429~450 (2010)
52. Chen, Y., "Social prediction : a new research paradigm based on machine learning", The Journal of Chinese Sociology, 8(1), pp.1~21 (2021)
53. Illuminating the effects of the US-China tariffwar on China’s economy, "Chor, D.", NBER, ~, pp.NBER w29349 (2021)
54. Consoli, S., "Emotions in macroeconomic news and their impact on the European bond market", Journal of International Money and Finance, 118, pp.102472~ (2021)
55. Constant, A., "Im Angesicht der Krise : US-Präsidentschaftswahlen in transnationaler Sicht", DIW Wochenbericht, 44, pp.688~701 (2008)
56. Corak, M., "The intergenerational transmission of employers", Journal of Labor Economics, 29(1), pp.37~68 (2011)
57. Donaldson, D., "The view from above : Applications of satellite data in economics", Journal of Economic Perspectives, 30(4), pp.171~198 (2016)
58. Doshi-Velez, F., "Towards a rigorous science of interpretable machine learning" (2017)[DOI]
59. "A machine learning approach to census record linking" (2016)[URL]
60. "Automated census record linking: A machine learning approach" (2016)[URL]
61. Feldmeyer, D., "Using OpenStreetMap data and machine learning to generate socio-economic indicators", International Journal of Geo-Information, 9, pp.498~ (2020)[DOI]
62. Text as data, "Gentzkow, M.", NBER, ~, pp.NBER w23276 (2017)
63. Glaeser, E. L., "Big data and big cities : The promises and limitations of improved measures of urban life", Economic Inquiry, 56(1), pp.114~137 (2018)
64. Grimmer, J., "Machine learning for social science : An agnostic approach", Annual Review of Political Science, 24, pp.395~419 (2021)
65. Hamermesh, D. S., "Six decades of top economics publishing : Who and how?", Journal of Economic Literature, 51(1), pp.162~172 (2013)
66. Hansen, S., "The long-run information effect of central bank communication", Journal of Monetary Economics, 108, pp.185~202 (2019)
67. Hausler, J., "News-based sentiment analysis in real estate : A machine learning approach", Journal of Property Research, 35(4), pp.344~371 (2018)
68. Henderson, J. V., "The global distribution of economic activity : Nature, history, and the role of trade", The Quarterly Journal of Economics, 133, pp.357~406 (2018)
69. Henderson, J. V., "Measuring economic growth from outer space", American Economic Review, 102(2), pp.994~1028 (2012)
70. Hoberg, G., "Redefining financial constraints : A text-based analysis", The Review of Financial Studies, 28(5), pp.1312~1352 (2015)
71. Hofman, J. M., "Prediction and explanation in social systems", Science, 355, pp.486~488 (2017)
72. Jean N., "Combining satellite imagery and machine learning to predict poverty", Science, 353(6301), pp.790~794 (2016)
73. Katris, C., "Prediction of unemployment rates with time series and machine learning techniques", Computational Economics, 55(2), pp.673~706 (2020)
74. Kim, Y., "Artificial intelligence technology and social problem solving", Paper presented at at the meeting of General Conference on Emerging Arts of Research on Management and Administration (2018)
75. Kleinberg, J., "A guide to solving social problems with machine learning", Harvard Business Review (2016)
76. Kleinberg, J., "Prediction policy problems", American Economic Review, 105(5), pp.491~495 (2015)
77. Kleinberg, J., "Discrimination in the age of algorithms", Journal of Legal Analysis, 10, pp.113~174 (2018)
78. Kleinberg, J., "Discrimination in the age of algorithms", Journal of Legal Analysis, 10, pp.113~174 (2018)
79. Kreif, N., "Machine learning in policy evaluation: new tools for causal inference" (2019)[DOI]
80. Levy, D., "Economists in the 2008 financial crisis : Slow to see, fast to act", Journal of Financial Stability, 60, pp.100986~ (2020)[DOI]
81. Memon, S., "Machine learning for economists : An introduction", The Pakistan Development Review, 60(2), pp.201~211 (2021)
82. Mullainathan, S., "Machine learning : An applied econometric approach", Journal of Economic Perspectives, 31(2), pp.87~106 (2017)
83. Naik, N., "Computer vision uncovers predictors of physical urban change", Proceedings of the National Academy of Sciences, 114(29), pp.7571~7576 (2017)
84. Petalas, D. P., "Forecasted economic change and the self-fulfilling prophecy in economic decision-making", Plos one, 12(3), pp.e0174353~ (2017)[DOI]
85. Rahal, C., "Machine Learning in the Social Sciences: Amara’s Law and an inclination across Foster’s S-Curve?. Leverhulme Centre for Demographic Science and Nuffield College" (2021), University of Oxford
86. Risi, J., "Predicting history", Nature human behaviour, 3(9), pp.906~912 (2019)
87. Roemer, J. E., "Equality of opportunity" (1998), Harvard University Press
88. Saavedra, M., "A machine learning approach to improving occupational income scores", Explorations in Economic History, 75, pp.101304~ (2020)
89. Singh, A., "Equality of opportunity in rankings", Paper presented at Workshop on Prioritizing Online Content (WPOC) at NIPS (2017)
90. Sobek, M., "The comparability of occupations and the generation of income scores", Historical Methods : A Journal of Quantitative and Interdisciplinary History, 28(1), pp.47~51 (1995)
91. "Machine learning for public policy making: How to use data-driven predictive modeling for the social good" (2018)[URL]
92. Stief, K., "Public policy analytics: Code & context for data science in government", ~ (2021)
93. Subrahmanian, V. S., "Predicting human behavior : The next frontiers", Science, 355(6324), pp.489~489 (2017)
94. Varian, H. R., "Big data : New tricks for econometrics", Journal of Economic Perspective, 28, pp.3~28 (2014)
95. Vosters, K., "Is the simple law of mobility really a law? Testing Clark’s hypothesis", The Economic Journal, 128(612), pp.F404~F421 (2018)
96. Vosters, K., "Intergenerational persistence in latent socioeconomic status : evidence from Sweden and the United States", Journal of Labor Economics, 35(3), pp.869~901 (2017)
97. Walker, C. B., "Housing booms and media coverage", Applied Economics, 46(32), pp.3954~3967 (2014)
98. Zhou, Z. H., "A brief introduction to weakly supervised learning", National Science Review, 5(1), pp.44~53 (2018)

관련 논문AI추천