基于贝叶斯模型的中国房地产投资发展研究
Research on the Development of Real Estate Investment in China Based on Bayesian Models
摘要: 房地产投资的发展程度会受到很多因素的影响，而且这些因素并不是单独存在，是相互组成一个复杂的系统共同影响着中国大陆各地区的房地产投资发展在时空上演进、变化，本文运用BYM、FBM两种贝叶斯时空模型研究分析房地产投资的发展程度，分析各个影响因子的影响程度，最后根据DIC值评估两种模型的拟合优劣。除此之外，本文运用FBM模型研究了2012~2021年中国大陆地区各个省、自治区、直辖市房地产投资的总体发展热度以及局部发展趋势。
Abstract: The development degree of real estate investment will be affected by many factors, and these factors do not exist alone. They form a complex system and jointly affect the evolution and change of real estate investment development in Chinese Mainland in time and space. This paper uses BYM and FBM Bayesian space-time models to study and analyze the development degree of real estate investment and the impact degree of each influencing factor, Finally, evaluate the fitting quality of the two models based on the DIC value. In addition, this paper uses the FBM model to study the overall development heat and local development trend of real estate investment in Chinese Mainland from 2012 to 2021.
文章引用：牛佳安, 丁咏梅. 基于贝叶斯模型的中国房地产投资发展研究[J]. 运筹与模糊学, 2024, 14(2): 610-624. https://doi.org/10.12677/orf.2024.142164

参考文献

[1] 黄徐亮, 徐海东, 倪鹏飞. 带动还是挤出?房地产投资对其他固定资产投资的影响研究[J]. 经济学家, 2023(6): 78-87.
[2] 刘芸. 长三角重点城市房地产投资潜力评价[J]. 现代营销(下旬刊), 2023(9): 96-99.
[3] 韦兰宁, 唐晓莲. 房地产投资与就业关系研究[J]. 合作经济与科技, 2022(16): 64-66.
[4] Weinberg, D.H., Friedman, J.M. and Stephen, K.M. (1981) Intraurban Residential Mobility: The Role of Transactions Costs, Market Market Imperfections, and Household Disequilibrium. Journal of Urban Economics, 9, 332-348. [Google Scholar] [CrossRef] 
[5] 乔林, 孔淑红. 我国不同发展层次城市房价影响因素的差异分析及对策研究[J]. 特区经济, 2012(5): 210-212.
[6] 黄俊宝. 基于贝叶斯层次时空模型的中国区域科技创新能力研究[D]: [硕士学位论文]. 深圳: 深圳大学经济学院, 2020.
[7] Goovaerts, P. and Gebreab, S. (2008) How Does Poisson Krigring Compare to the Popular BYM Model for Mapping Disease Risks? International Journal of Health Geographics, 7, Article No. 6. [Google Scholar] [CrossRef] 
[8] 李世元, 王学梅. 贝叶斯时空模型在空间流行病学中的研究进展[J]. 世界最新医学信息文摘, 2017, 17(34): 55-57.
[9] Wang, W., Li, J., et al. (2022) Spatiotemporal Trends and Ecological Determinants of Cardiovascular Mortality among 2844 Counties in Mainland China, 2006-2020: A Bayesian Modeling Study of National Mortality Registries. BMC Medicine, 20, Article No. 467. [Google Scholar] [CrossRef] [PubMed]
[10] Congdon, P. (2000) Monitoring Suicide Mortality: A Bayesian Approach. European Journal of Population/Revue Européenne de Démographie, 16, 251-284. [Google Scholar] [CrossRef] 
[11] Haining, R., Law, J. and Griffith, D. (2009) Modelling Small Area Counts in the Presence of Overdispersion and Spatial Autocorrelation. Computational Statistics & Data Analysis, 53, 2923-2937. [Google Scholar] [CrossRef] 
[12] Yu, Q., Scribner, R., Carlin, B., et al. (2008) Multilevel Spatio-Temporal Dual Changepoint Models for Relating Alcohol Outlet Destruction and Changes in Neighborhood Rates of Assaultive Violence. Geospatial Health, 2, 161-172. [Google Scholar] [CrossRef] [PubMed]
[13] Bernardinelli, L., Clayton, D., Pascutto, C., et al. (1995) Bayesian Analysis of Space-Time Variation in Disease Risk. Statistics in Medicine, 14, 2433-2443. [Google Scholar] [CrossRef] [PubMed]
[14] Kleinschmidt, I., Sharp, B., Mueller, I., et al. (2002) Rise in Malaria Incidence Rates in South Africa: A Small-Area Spatial Analysis of Variation in Time Trends. American Journal of Epidemiology, 155, 257-264. [Google Scholar] [CrossRef] [PubMed]
[15] Aguero-Valverde, J. and Jovanis, P.P. (2006) Spatial Analysis of Fatal and Injury Crashes in Pennsylvania. Accident Analysis & Prevention, 38, 618-625. [Google Scholar] [CrossRef] [PubMed]
[16] Osnas, E.E., Heisey, D.M., Rolley, R.E., et al. (2009) Spatial and Temporal Patterns of Chronic Wasting Disease: Fine-Scale Mapping of a Wildlife Epidemic in Wisconsin. Ecological Applications, 19, 1311-1322. [Google Scholar] [CrossRef] [PubMed]
[17] Law, J., Quick, M. and Chan, P. (2014) Bayesian Spatio-Temporal Modeling for Analysing Local Patterns of Crime over Time at the Small-Area Level. Journal of Quantitative Criminology, 30, 57-78. [Google Scholar] [CrossRef] 
[18] Li, J. and Han, X. (2023) Spatiotemporal Evolution and Drivers of Total Health Expenditure across Mainland China in Recent Years. International Journal of Environmental Research and Public Health, 20, Article 597. [Google Scholar] [CrossRef] [PubMed]
[19] 韩秀兰, 李俊明. 中国城市房价时空特征与影响机制研究——基于贝叶斯分域时空模型的实证[J]. 数理统计与管理, 2018, 37(5): 940-950.
[20] 郑杨, 李晓松. 贝叶斯时空模型在疾病时空数据分析中的应用[J]. 中华预防医学杂志, 2010, 44(12): 1136-1139.
[21] Richardson, S., Thomson, A., Best, N., et al. (2004) Interpreting Posterior Relative Risk Estimates in Disease-Mapping Studies. Environmental Health Perspectives, 112, 1016-1025. [Google Scholar] [CrossRef] [PubMed]

为你推荐