node12 Lab 5 - ACF and PACF Plots STAT 510 - Applied Time Series Analysis

Node12 Lab 5 - ACF and PACF Plots STAT 510 - Applied Time Series Analysis

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This is Google's cache of http://onlinecourses.science.psu.edu/stat510/node/12 . It is a snapshot of the page as it appeared on 27 Aug 2010 05:40:20 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 5 - ACF and PACF Plots The ACF can be used to identify the order of a MA model. We know that the true ACF of a pure MA ( q ) model will drop to zero after the q th lag. (This is not always easy to see in practice due to the fluctuations in the empirical ACF.) An AR ( p ) or an ARMA ( p , q ) with p greater than or equal to one will generate an ACF plot that tails of exponentially. This is due to the causal property of these models. This is good in that we can tell the qualitative difference between MA ( q ) on one hand and the AR ( p ) / ARMA ( p , q ) on the other. If we think that the correct model is
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Node12 Lab 5 - ACF and PACF Plots STAT 510 - Applied Time Series Analysis

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