Assessing the Forecasting Performance of Regime‐Switching, ARIMA and GARCH Models of House Prices
比较了ARIMA、GARCH和体制转换三种时间序列模型在预测房价上的表现，发现虽然体制转换模型在样本内表现更好，但简单的ARIMA模型在样本外预测中通常更优。
While price changes on any particular home are difficult to predict, aggregate home price changes are forecastable. In this context, this paper compares the forecasting performance of three types of univariate time series models: ARIMA, GARCH and regime‐switching. The underlying intuition behind regime‐switching models is that the series of interest behaves differently depending on the realization of an unobservable regime variable. Regime‐switching models are a compelling choice for real estate markets that have historically displayed boom and bust cycles. However, we find that, while regime‐switching models can perform better in‐sample, simple ARIMA models generally perform better in out‐of‐sample forecasting.