基于混合核函数SVM修正MIDAS模型的房地产景气指数预测研究

Study on Forecast of Real Estate Prosperity Index Based on MIDAS Model Corrected by Mixed Kernel Function SVM

摘要: 房地产市场具有影响因素多样性、非线性波动等特点，随着相关数据信息可获得性的进一步提高，因素间存在的频率差、相互干扰等问题不可忽视。在以往研究的基础上，综合考虑时效性强的百度指数以及关联度高的房地产市场相关指标，构建MIDAS (混频数据处理模型)模型解决百度指数与房地产景气指数间频率差的问题，建立随机森林的封装筛选模型实现重要特征的选择，并基于混合核的SVR (支持向量机)对MIDAS预测结果进行进一步修正。实证表明，本文模型充分利用了混频数据信息，也符合房地产市场非线性波动的特点，在预测精度与误差波动性上都得到了一定提升。

Abstract: The real estate market has the characteristics of multi influence factors and nonlinear fluctuations. With the further increase of the availability of relevant data information, the frequency difference and mutual interference between the factors cannot be ignored. Based on the existing research, we considered both the Baidu index with strong timeliness and related indicators of the real estate market with high relevance; we built the MIDAS model to solve the problem of frequency difference between Baidu index and real estate prosperity index, and established an optional packing and screening model random forests for the selection of features. Finally, based on the hybrid kernel SVR, we further modified the MIDAS prediction results. The empirical results showed that the model made full use of the mixed data information and also met the characteristics of the nonlinear fluctuations in the real estate market. The proposed model improved both the accuracy of forecasting and the volatility of the error.