Solving Euler equations via two-stage nonparametric penalized splines
Solving Euler equations via two-stage nonparametric penalized splines
复制标题
通过两阶段非参数惩罚样条求解欧拉方程
DOI：
10.1016/j.jeconom.2020.04.042
复制
发表时间：
2020
影响因子：
6.3
通讯作者：
Yingxing Li
中科院分区：
文献类型：
--
作者：
Liyuan Cui;Yongmiao Hong;Yingxing Li
本研究提出一种新的估计为基础的方法来解决资产定价模型的平稳和随时间变化的观察。我们的方法是强大的误指定错误，同时继承了封闭形式的解决方案。通过将Euler方程表示为适定的第二类积分方程，提出了一种惩罚两阶段非参数估计方法，并在较弱的条件下证明了其最优收敛性.由于惩罚样条的优点，我们的估计对样条设置不太敏感，我们还设计了一个快速的数据驱动算法来有效地调整关键平滑器，即惩罚量。我们的方法具有良好的有限样本性能。使用美国1947年至2017年的数据，我们重新研究了回报的可预测性，发现估计的隐含股息收益率在短期内显著预测了较低的未来现金流和较高的利率。
This study proposes a novel estimation-based approach to solving asset pricing models for both stationary and time-varying observations. Our method is robust to misspecification errors while inheriting a closed-form solution. By representing the Euler equation into a well-posed integral equation of the second kind, we propose a penalized two-stage nonparametric estimation method and establish its optimal convergence under mild conditions. With the merit of penalized splines, our estimate is less sensitive to the spline setting and we also design a fast data-driven algorithm to effectively tune the key smoother, i.e. the penalty amount. Our approach exhibits excellent finite sample performance. Using the US data from 1947 to 2017, we reinvestigate the return predictability and find that the estimated implied dividend yield significantly predicts lower future cash flows and higher interest rates at short horizons.