Inference for impulse response coefficients from multivariate fractionally integrated processes
研究了多元分数积分时间序列系统中脉冲响应系数及其置信区间的估计方法，扩展了单变量分析，采用基于向量自回归近似的半参数时域估计器，并发现应用Kilian小样本偏差修正能改善置信区间覆盖精度。
This article considers a multivariate system of fractionally integrated time series and investigates the most appropriate way for estimating Impulse Response (IR) coefficients and their associated confidence intervals. The article extends the univariate analysis recently provided by Baillie and Kapetanios (2013 Baillie, R. T., Kapetanios, G. (2013). Estimation and inference for impulse response functions form univariate strongly persistent processes. Econometrics Journal 16:373–399.[Crossref], [Web of Science ®] , [Google Scholar]), and uses a semiparametric, time domain estimator, based on a vector autoregression (VAR) approximation. Results are also derived for the orthogonalized estimated IRs which are generally more practically relevant. Simulation evidence strongly indicates the desirability of applying the Kilian small sample bias correction, which is found to improve the coverage accuracy of confidence intervals for IRs. The most appropriate order of the VAR turns out to be relevant for the lag length of the IR being estimated.