Modeling and forecasting short-term interest rates: The benefits of smooth regimes, macroeconomic variables, and bagging
提出一种平滑转换树模型，同时建模短期利率的条件均值和方差，发现通胀和实际活动的领先指标是刻画多机制结构的最相关预测因子，且最优模型具有三个限制性机制，结合装袋法能有效预测前两个条件矩。
In this paper we propose a smooth transition tree model for both the conditional mean and variance of the short-term interest rate process. The estimation of such models is addressed and the asymptotic properties of the quasi-maximum likelihood estimator are derived. Model specification is also discussed. When the model is applied to the US short-term interest rate we find (1) leading indicators for inflation and real activity are the most relevant predictors in characterizing the multiple regimes’ structure; (2) the optimal model has three limiting regimes. Moreover, we provide empirical evidence of the power of the model in forecasting the first two conditional moments when it is used in connection with bootstrap aggregation (bagging).