文章基本信息

标题：Autocorrelation robust inference using the Daniell kernel with fixed bandwidth
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作者：Javier Hualde ; Fabrizio Iacone
期刊名称：Discussion Papers in Economics / Department of Economics, University of York
出版年度：2015
卷号：2015
出版社：University of York
摘要：We consider alternative asymptotics for frequency domain estimates of the long run variance, in which the bandwidth is kept fixed. For a weakly dependent process, this does not yield a consistent estimateof the long run variance, but the standardized mean has t limit distribution, which, for any given bandwidth, appears to be more precise than the traditional Gaussian limit. In presence of fractionally integrated data, the limit distribution of the estimate is not standard, and we derive critical values for the standardized mean for various bandwidths. Again, we find that this asymptotic result provides a better approximation than other proposals like the Memory Autocorrelation Consistent (MAC) estimate. In multivariate set up, fixed bandwidth asymptotics may be also used to provide a characterization to the limit distribution of estimates of cointegrating parameter which differs substantially from the conventional Narrow Band asymptotics.
关键词：long run variance estimation; long memory; large-m and fixed-masymptotic theory