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node16 Lab 9 - Estimating Spectral Density STAT 510 - Applied Time Series Analysis

# Node16 Lab 9 - Estimating Spectral Density STAT 510 - Applied Time Series Analysis

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This is Google's cache of http://onlinecourses.science.psu.edu/stat510/node/16 . It is a snapshot of the page as it appeared on 19 Jul 2010 07:19:42 GMT. The current page could have changed in the meantime. Learn more Text-only version STAT 510 - Applied Time Series Analysis ANGEL Department of Statistics Eberly College of Science Home Lab 9 - Estimating Spectral Density Let's look at the Australian labour data ( labour.dat ) which was seen in previously. We would like to estimate the spectral density of the data. We may generate the raw periodogram using the following code: labour<-scan("labour.dat") labour<-labour[1:144] tlabour<-diff(diff(log(labour)),12) labourpgram<-spec.pgram(tlabour,taper=0, log="no") It is pretty hard to tell what's going on with the unsmoothed periodogram. We will start by adding some smoothing with the following command: spec.pgram(tlabour, spans=3, taper=0, log="no") The argument "spans" corresponds to the "L" that we've been using in class which means that this will be the total window size used in the argument. By default the modified Daniell kernel is used. If we would

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