关键词: 高维稀疏模型, 惩罚函数, 模型选择, 房价预测

Abstract: High-dimensional sparse modeling is one of the cutting-edge issues in contemporary statistics and econometrics, which is a kind of statistical methodology for the analysis of big data and will be widely used in the fields of economics and finance. This paper explores the problems and challenges of high-dimensional data and high-dimensional model for the traditional methods, and reviews the development of sparse modeling, the role of selection mechanism as well as the theoretical properties of penalty function methods. In empirical application, we use a high-dimensional sparse VAR model to study the problem of real estate prices forecasting in 35 cities. Compared to the traditional VAR model and low-dimensional dynamic panel data model, the structure of the high-dimensional sparse VAR model is more simple and able to capture important explanatory variables and economic information. In addition, it also has a satisfactory out-of sample predictive ability, which induces a smaller forecasting bias than the traditional models.

Key words: High-dimensional Sparse Model, Penalty Function, Model Selection, Real Estate Prices Forecasting