Advanced Search
MyIDEAS: Login

The Linear Regression Model With Autocorrelated Errors: Just Say No To Error Autocorrelation

Contents:

Author Info

  • McGuirk, Anya M.
  • Spanos, Aris

Abstract

This paper focuses on the practice of serial correlation correcting of the Linear Regression Model (LRM) by modeling the error. Simple Monte Carlo experiments are used to demonstrate the following points regarding this practice. First, the common factor restrictions implicitly imposed on the temporal structure of yt and xt appear to be completely unreasonable for any real world application. Second, when one compares the Autocorrelation-Corrected LRM (ACLRM) model estimates with estimates from the (unrestricted) Dynamic Linear Regression Model (DLRM) encompassing the ACLRM there is no significant gain in efficiency! Third, as expected, when the common factor restrictions do not hold the LRM model gives poor estimates of the true parameters and estimation of the ACLRM simply gives rise to different misleading results! On the other hand, estimates from the DLRM and the corresponding VAR model are very reliable. Fourth, the power of the usual Durbin Watson test (DW) of autocorrelation is much higher when the common factor restrictions do hold than when they do not. But, a more general test of autocorrelation is shown to perform almost as well as the DW when the common factor restrictions do hold and significantly better than the DW when the restrictions do not hold. Fifth, we demonstrate how simple it is to, at least, test the common factor restrictions imposed and we illustrate how powerful this test can be.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://purl.umn.edu/19905
Download Restriction: no

Bibliographic Info

Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2002 Annual meeting, July 28-31, Long Beach, CA with number 19905.

as in new window
Length:
Date of creation: 2002
Date of revision:
Handle: RePEc:ags:aaea02:19905

Contact details of provider:
Postal: 555 East Wells Street, Suite 1100, Milwaukee, Wisconsin 53202
Phone: (414) 918-3190
Fax: (414) 276-3349
Email:
Web page: http://www.aaea.org
More information through EDIRC

Related research

Keywords: Research Methods/ Statistical Methods;

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Sargan, J D, 1980. "Some Tests of Dynamic Specification for a Single Equation," Econometrica, Econometric Society, vol. 48(4), pages 879-97, May.
  2. Spanos, Aris, 1995. "On theory testing in econometrics : Modeling with nonexperimental data," Journal of Econometrics, Elsevier, vol. 67(1), pages 189-226, May.
  3. repec:cup:cbooks:9780521424080 is not listed on IDEAS
  4. Hendry, David F & Mizon, Grayham E, 1978. "Serial Correlation as a Convenient Simplification, not a Nuisance: A Comment on a Study of the Demand for Money by the Bank of England," Economic Journal, Royal Economic Society, vol. 88(351), pages 549-63, September.
  5. Hoover, Kevin D, 1988. "On the Pitfalls of Untested Common-Factor Restrictions: The Case of the Inverted Fisher Hypothesis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 50(2), pages 125-38, May.
  6. Spanos, Aris & McGuirk, Anya, 2002. "The problem of near-multicollinearity revisited: erratic vs systematic volatility," Journal of Econometrics, Elsevier, vol. 108(2), pages 365-393, June.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Sriananthakumar, Sivagowry, 2013. "Testing linear regression model with AR(1) errors against a first-order dynamic linear regression model with white noise errors: A point optimal testing approach," Economic Modelling, Elsevier, vol. 33(C), pages 126-136.
  2. Nivens, Heather D. & Kastens, Terry L. & Dhuyvetter, Kevin C. & Featherstone, Allen M., 2002. "Using Satellite Imagery In Predicting Kansas Farmland Values," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 27(02), December.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:ags:aaea02:19905. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.