A first order continuous timeVARwith random coefficients
研究了一类系数随机变化的一阶连续时间向量自回归模型，提出基于近似离散模型的估计方法，通过蒙特卡洛实验验证有限样本表现，并应用于利率期限结构预期理论，发现允许系数时变能大幅降低参数估计的均方根误差。
This article considers a first order continuous time vector autoregression with random coefficients. We discuss some difficulties that arise when the exact discrete analogue is used for estimating the continuous time parameters and provide an estimation method based on an approximate discrete model. Some expressions for the estimator of the drift parameter matrix, for its approximated bias and for the covariance matrix of the parameter estimates are derived. The finite sample performance of the proposed method is studied by a Monte Carlo experiment. We also illustrate the advantages of our model in an application on the expectations theory of the term structure of interest rates. Results show that the performance of the proposed methodology is good, and allowing for time variation on coefficients results in large reductions in the root mean square error of the parameter estimates.