응용통계연구 (The Korean Journal of Applied Statistics)

  • 제30권2호
  • /
  • Pages.215-231
  • /
  • 2017
  • /
  • 1225-066X(pISSN)
  • /
  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

미국 대통령 예비선거에 적용한 시공간 의존성을 고려한 자기로지스틱 회귀모형 연구

Autologistic models with an application to US presidential primaries considering spatial and temporal dependence

  • 염호정 (연세대학교 정보산업공학과) ;
  • 이원경 (연세대학교 정보산업공학과) ;
  • 손소영 (연세대학교 정보산업공학과)
  • Yeom, Ho Jeong (Department of Information and Industrial Engineering, Yonsei University) ;
  • Lee, Won Kyung (Department of Information and Industrial Engineering, Yonsei University) ;
  • Sohn, So Young (Department of Information and Industrial Engineering, Yonsei University)
  • 투고 : 2017.01.05
  • 심사 : 2017.03.16
  • 발행 : 2017.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

미국 대통령 예선은 선거인단이 시차를 두고 여러 회에 걸쳐 진행되는 특징이 있음에도 많은 연구가 진행되지 않았다. 본 연구에서는 다양한 자기로지스틱 모형을 통해 미국 대통령 예비선거 결과와 사회경제적 변수간의 시공간 의존성의 관계를 파악하고자 한다. 2016년 데이터에 적용한 분석결과 각 카운티의 노년층, 흑인, 여성 그리고 히스패닉 인구 비율이 높은 지역일수록 힐러리 클린턴을 지지할 확률이 높은 것으로 나타났다. 또한, 주변 카운티에서 많은 지지를 받은 후보가 이웃 지역에서도 많이 지지를 받을 확률이 높고 이전 선거에서 많은 지지를 받는 것과 다음 선거 지역의 결과 간의 상관관계도 확인되었다. 시공간 의존성을 알아보기 위한 모형 중에서 슈퍼화요일의 선거 결과가 이후 선거와 관련이 있다고 가정한 모형의 설명력이 가장 높은 것으로 판명되었다.

The US presidential primaries take place sequentially in different places with a time lag. However, they have not attracted as much attention in terms of modelling as the US presidential election has. This study applied several autologistic models to find the relation between the outcome of the primary election for a Democrat candidate with socioeconomic attributes in consideration of spatial and temporal dependence. According to the result applied to the 2016 election data at the county level, Hillary Clinton was supported by people in counties with high population rates of old age, Black, female and Hispanic. In addition, spatial dependence was observed, representing that people were likely to support the same candidate who was supported from neighboring counties. Positive auto-correlation was also observed in the time-series of the election outcome. Among several autologistic models of this study, the model specifying the effect of Super Tuesday had the best fit.

키워드

참고문헌

  1. Abramowitz, A. I. (2008). Forecasting the 2008 presidential election with the time-for-change model, PS: Political Science and Politics, 41, 691-695. https://doi.org/10.1017/S1049096508081249
  2. Augustin, N. H., Mugglestone, M. A., and Buckland, S. T. (1996). An autologistic model for the spatial distribution of wildlife, Journal of Applied Ecology, 33, 339-347. https://doi.org/10.2307/2404755
  3. Austin, M. P. (2002). Spatial prediction of species distribution: an interface between ecological theory and statistical modelling, Ecological Modelling, 157, 101-118. https://doi.org/10.1016/S0304-3800(02)00205-3
  4. Bartels, L. M. (1987). Candidate choice and the dynamics of the presidential nominating process, American Journal of Political Science, 31, 1-30. https://doi.org/10.2307/2111322
  5. Besag, J. E. (1972). Nearest-neighbour systems and the auto-logistic model for binary data, Journal of the Royal Statistical Society Series B (Methodological), 34, 75-83.
  6. Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems (with discussion), Journal of the Royal Statistical Society B (Methodological), 36, 192-236.
  7. Buckland, S. T. and Elston, D. A. (1993). Empirical models for the spatial distribution of wildlife, Journal of Applied Ecology, 30, 478-495. https://doi.org/10.2307/2404188
  8. Caragea, P. C. and Kaiser, M. S. (2009). Autologistic models with interpretable parameters, Journal of Agricultural, Biological, and Environmental Statistics, 14, 281-300. https://doi.org/10.1198/jabes.2009.07032
  9. Cho, S. D. (2009). Voter choice in primary: the 2008 democratic primary election in the United States, Korean Political Science Review, 43, 193-214. https://doi.org/10.18854/kpsr.2009.43.2.009
  10. Cliff, A. D. and Ord, J. K. (1981). Spatial Processes: Models & Applications, Pion, London.
  11. Gumpertz, M. L., Graham, J. M., and Ristaino, J. B. (1997). Autologistic model of spatial pattern of Phytophthora epidemic in bell pepper: effects of soil variables on disease presence, Journal of Agricultural, Biological, and Environmental Statistics, 2, 131-156. https://doi.org/10.2307/1400400
  12. He, F., Zhou, J., and Zhu, H. (2003). Autologistic regression model for the distribution of vegetation, Journal of Agricultural, Biological, and Environmental Statistics, 8, 205-222. https://doi.org/10.1198/1085711031508
  13. Kim, D. H., Kang, K. Y., and Sohn, S. Y. (2016). Spatial pattern analysis of CO2 emission in Seoul metropolitan city based on a geographically weighted regression, Journal of the Korean Institute of Industrial Engineers, 42, 96-111. https://doi.org/10.7232/JKIIE.2016.42.2.096
  14. Kim, D. Y. (2012). White voters' choice in the 2008 U.S. presidential election, Journal of International Area Studies, 16, 3-22.
  15. Kim, J. W. (2013). The "44% controversy" over the 2004 presidential election, The Korean Journal of American History, 38, 219-248.
  16. Lewis-Beck, M. S. (2005). Election forecasting: principles and practice, The British Journal of Politics and International Relations, 7, 145-164. https://doi.org/10.1111/j.1467-856X.2005.00178.x
  17. Lewis-Beck, M. S. and Tien, C. (2008). Forecasting presidential elections: when to change the model, In- ternational Journal of Forecasting, 24, 227-236. https://doi.org/10.1016/j.ijforecast.2008.02.008
  18. Norpoth, H. (2004). From primary to general election: a forecast of the presidential vote, Political Science and Politics, 37, 737-740.
  19. Norrander, B. (1993). Nomination choices: caucus and primary outcomes 1976-88, American Journal of Political Science, 37, 343-364. https://doi.org/10.2307/2111376
  20. Park, J. (2015). Review of spatial linear mixed models for non-gaussian outcomes, Korean Journal of Applied Statistics, 28, 353-360. https://doi.org/10.5351/KJAS.2015.28.2.353
  21. Sherman, M., Apanasovich, T. V., and Carroll, R. J. (2006). On estimation in binary autologistic spatial models, Journal of Statistical Computation and Simulation, 76, 167-179. https://doi.org/10.1080/00949650412331320873
  22. Steger, W. P. (2007). Who wins nominations and why? An updated forecast of the presidential primary vote, Political Research Quarterly, 60, 91-99. https://doi.org/10.1177/1065912906298597
  23. Wu, H. and Huffer, F. R. W. (1997). Modelling the distribution of plant species using the autologistic regression model, Environmental and Ecological Statistics, 4, 31-48. https://doi.org/10.1023/A:1018553807765