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by: Nikita Hendricks

Autocorrelation 2305

Marketplace > Baylor University > Business > 2305 > Autocorrelation
Nikita Hendricks


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About this Document

Prof. Turner
Class Notes
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This 1 page Class Notes was uploaded by Nikita Hendricks on Sunday October 9, 2016. The Class Notes belongs to 2305 at Baylor University taught by Prof. Turner in Fall 2016. Since its upload, it has received 3 views. For similar materials see QBA in Business at Baylor University.


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Date Created: 10/09/16
QBA Autocorrelation When an AR model is correctly specified, the residual terms will not exhibit serial correlation. Serial correlation means the error terms are positively or negatively correlated. When the error terms are correlated, standard errors are unreliable and t-tests of individual coefficients can incorrectly show statistical significance or insignificance. If the residuals have significant autocorrelation, the AR model that produced the residuals is not the best model for the time series being analyzed. The procedure to test whether an AR time series model is correctly specified involves three steps: 1. Estimate the AR model being evaluated using linear regression: start with a first order AR model using xt = b0 + b1xt-1 + εt. 2. Calculate the autocorrelations of the models residuals (i.e. the level of correlation between the forecast errors from one period to the next). 3. Test whether the autocorrelations are significantly different from zero: if the model is correctly specified, none of the autocorrelations will be statistically significant. To test for significance, a t-test is used to test the hypotheses that the correlations of the residuals are zero.


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