Housing Price Forecastability: A Factor Analysis
使用主成分分析、偏最小二乘和稀疏偏最小二乘方法，利用128个宏观经济时间序列预测美国房价，发现偏最小二乘模型优于主成分分析模型，且具有显著的样本外预测能力。
Abstract We examine U.S. housing price forecastability using principal component analysis (PCA), partial least squares (PLS) and sparse PLS (SPLS). We incorporate information from a large panel of 128 economic time series and show that macroeconomic fundamentals have strong predictive power for future movements in housing prices. We find that (S)PLS models systematically dominate PCA models. (S)PLS models also generate significant out‐of‐sample predictive power over and above the predictive power contained by the price–rent ratio, autoregressive benchmarks and regression models based on small datasets.