CHARACTERISTIC FUNCTION–BASED TESTING FOR MULTIFACTOR CONTINUOUS-TIME MARKOV MODELS VIA NONPARAMETRIC REGRESSION
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通过非参数回归与特征函数检验多因子连续时间马尔可夫模型
- 研究问题
- 如何检验多因子连续时间马尔可夫模型的联合条件分布，并识别各分量动态看似正确、但联合动态仍然错设的情况？
- 方法与思路
- 作者基于条件特征函数和非参数回归构造整体拟合检验及诊断程序，允许部分数据依赖带宽，并考察参数自助法。
- 材料陈述的贡献
- 摘要称统计量在正确设定下渐近标准正态，模拟可检出联合动态错设；参数自助法改善有限样本表现但增加计算成本。
- 阅读关注点
- 应核对观测机制、条件特征函数近似误差、降维方式及带宽条件，并检查诊断程序能区分哪些具体错设来源。
- 阅读边界
- 仅据所给 Crossref 摘要，无法确认检验条件和模拟改善幅度；多因子状态过程不能直接视为所列金融因子模型。
原文摘要
We develop a nonparametric regression-based goodness-of-fit test for multifactor continuous-time Markov models using the conditional characteristic function, which often has a convenient closed form or can be approximated accurately for many popular continuous-time Markov models in economics and finance. An omnibus test fully utilizes the information in the joint conditional distribution of the underlying processes and hence has power against a vast class of continuous-time alternatives in the multifactor framework. A class of easy-to-interpret diagnostic procedures is also proposed to gauge possible sources of model misspecification. All the proposed test statistics have a convenient asymptotic N (0, 1) distribution under correct model specification, and all asymptotic results allow for some data-dependent bandwidth. Simulations show that in finite samples, our tests have reasonable size, thanks to the dimension reduction in nonparametric regression, and good power against a variety of alternatives, including misspecifications in the joint dynamics, but the dynamics of each individual component is correctly specified. This feature is not attainable by some existing tests. A parametric bootstrap improves the finite-sample performance of proposed tests but with a higher computational cost.