The estimation of continuous time models with mixed frequency data
推导了由多元连续时间模型生成的离散时间混合频率数据的精确表示，允许股票和流量变量组合及确定性趋势，并利用蒙特卡洛模拟评估了基于混合频率数据的连续时间系统参数极大似然估计的有限样本表现。
This paper derives exact representations for discrete time mixed frequency data generated by an underlying multivariate continuous time model. Allowance is made for different combinations of stock and flow variables as well as deterministic trends, and the variables themselves may be stationary or nonstationary (and possibly cointegrated). The resulting discrete time representations allow for the information contained in high frequency data to be utilised alongside the low frequency data in the estimation of the parameters of the continuous time model. Monte Carlo simulations explore the finite sample performance of the maximum likelihood estimator of the continuous time system parameters based on mixed frequency data, and a comparison with extant methods of using data only at the lowest frequency is provided. An empirical application demonstrates the methods developed in the paper and it concludes with a discussion of further ways in which the present analysis can be extended and refined.