Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series
Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series
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DOI：
10.1080/07474938.2014.976527
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发表时间：
2016-07
影响因子：
1.2
通讯作者：
J. Miller
中科院分区：
文献类型：
--
作者：
J. Miller
本文分析了当回归和和回归量在不同频率下观测时，协整向量的有效估计。以前的作者已经研究了特定的时间聚集或采样方案的影响，发现传统的有效技术是有效的，只有当回归和回归量平均采样。使用另一种方法来分析聚合下更一般的加权方案，我推导出一个效率界，这是有条件的聚合的类型上使用的低频系列和不同的全信息高频数据生成过程，这是不可行的，由于聚合的至少一个系列定义的无条件界。我修改了传统的估计，典型的协整回归（CCR），以适应的情况下，聚合权重是已知的。相关性结构可以被用来抵消来自聚合的潜在信息损失，从而产生有条件有效的估计器。在未知权重的情况下，误差项的相关结构通常会混淆有条件有效权重的识别。效率说明使用模拟研究和应用程序估计汽油需求方程。
I analyze efficient estimation of a cointegrating vector when the regressand and regressor are observed at different frequencies. Previous authors have examined the effects of specific temporal aggregation or sampling schemes, finding conventionally efficient techniques to be efficient only when both the regressand and the regressors are average sampled. Using an alternative method for analyzing aggregation under more general weighting schemes, I derive an efficiency bound that is conditional on the type of aggregation used on the low-frequency series and differs from the unconditional bound defined by the full-information high-frequency data-generating process, which is infeasible due to aggregation of at least one series. I modify a conventional estimator, canonical cointegrating regression (CCR), to accommodate cases in which the aggregation weights are known. The correlation structure may be utilized to offset the potential information loss from aggregation, resulting in a conditionally efficient estimator. In the case of unknown weights, the correlation structure of the error term generally confounds identification of conditionally efficient weights. Efficiency is illustrated using a simulation study and an application to estimating a gasoline demand equation.