A simple message for autocorrelation correctors: Don't

by: GE Mizon

Journal of Econometrics (September 1995), pp. 267-288.

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Abstract

Though the practice of 'correcting for residual autocorrelation' has long been critized, it is still commonly advocated and followed. A simple example shows that even when a linear regression model has first-order autoregressive errors, it is possible for autoregressive least squares estimation (e.g., Cochrane-Orcutt) to yield inconsistent estimates. This dramatically illustrates that 'autocorrelation correction' is invalid in general, and cannot be justified on the grounds of 'robustifying' estimation against the presence of residual serial correlation. Invalid common factors in I(1) systems also have adverse effects on inference. A 'general-to-specific' modelling strategy applied to the observed modelled variables avoids these difficulties.

@article{citeulike:3033161, abstract = {Though the practice of 'correcting for residual autocorrelation' has long been critized, it is still commonly advocated and followed. A simple example shows that even when a linear regression model has first-order autoregressive errors, it is possible for autoregressive least squares estimation (e.g., Cochrane-Orcutt) to yield inconsistent estimates. This dramatically illustrates that 'autocorrelation correction' is invalid in general, and cannot be justified on the grounds of 'robustifying' estimation against the presence of residual serial correlation. Invalid common factors in I(1) systems also have adverse effects on inference. A 'general-to-specific' modelling strategy applied to the observed modelled variables avoids these difficulties.}, author = {Mizon, G. E. }, citeulike-article-id = {3033161}, doi = {http://dx.doi.org/10.1016/0304-4076(94)01671-L}, issn = {0304-4076}, journal = {Journal of Econometrics}, keywords = {autocorrelation}, month = {September}, pages = {267--288}, posted-at = {2008-07-25 15:12:39}, priority = {2}, publisher = {Elsevier}, title = {A simple message for autocorrelation correctors: Don\&\#039;t}, url = {http://dx.doi.org/10.1016/0304-4076(94)01671-L}, year = {1995} }

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TY - JOUR ID - citeulike:3033161 L3 - citeulike-article-id:3033161 TI - A simple message for autocorrelation correctors: Don't JF - Journal of Econometrics SP - 267 EP - 288 PB - Elsevier SN - 0304-4076 N2 - Though the practice of 'correcting for residual autocorrelation' has long been critized, it is still commonly advocated and followed. A simple example shows that even when a linear regression model has first-order autoregressive errors, it is possible for autoregressive least squares estimation (e.g., Cochrane-Orcutt) to yield inconsistent estimates. This dramatically illustrates that 'autocorrelation correction' is invalid in general, and cannot be justified on the grounds of 'robustifying' estimation against the presence of residual serial correlation. Invalid common factors in I(1) systems also have adverse effects on inference. A 'general-to-specific' modelling strategy applied to the observed modelled variables avoids these difficulties. KW - autocorrelation AU - Mizon, GE PY - 1995/09// UR - http://dx.doi.org/10.1016/0304-4076(94)01671-L ER -

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