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Collinearity Diagnostics in gretl


  • Lee C. Adkins

    (Oklahoma State University)

  • Melissa S. Waters

    (Southern University)

  • R. Carter Hill

    (Louisiana State University)


Collinearity is blamed for all sorts of trouble in empirical work: inconclusive or weak results, unexpected signs on coefficients, and general computational mayhem in nonlinear estimators. Collinearity is a matter of degree since perfect col linearity has a perfectly easy solution. Near perfect col linearity can be vexing however since it makes precise measurement of model parameters particularly difficult in some cases. A number of methods for detecting col-linearity have been proposed. Some of these are useful, others not. Hill and Adkins (2001) summarize the good and bad based on much of the relevant literature up to 2001. They also make some recommendations for the detection and amelioration of inadequate variation in the data. The purpose of our paper is twofold: 1) update any significant cant findings on col-linearity since the Hill and Adkins (2001) survey and 2) to write and document gretl functions that perform several regression diagnostic procedures not already present in the software. These include the diagnostics suggested in Hill and Adkins (2001). In particular, we introduce hansl routines to perform the variance decomposition of Belsely, Kuh, and Welch (1980) for both linear and nonlinear models and provide a function to compute critical values for the Belsley (1982) signal-to-noise ratio test. The use of these is explored in several examples.

Suggested Citation

  • Lee C. Adkins & Melissa S. Waters & R. Carter Hill, 2015. "Collinearity Diagnostics in gretl," Economics Working Paper Series 1506, Oklahoma State University, Department of Economics and Legal Studies in Business.
  • Handle: RePEc:okl:wpaper:1506

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    References listed on IDEAS

    1. Belsley, David A., 1982. "Assessing the presence of harmful collinearity and other forms of weak data through a test for signal-to-noise," Journal of Econometrics, Elsevier, vol. 20(2), pages 211-253, November.
    2. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," Advances in Econometrics, in: 30th Anniversary Edition, pages 171-196, Emerald Group Publishing Limited.
    3. Friendly, Michael & Kwan, Ernest, 2009. "Where's Waldo? Visualizing Collinearity Diagnostics," The American Statistician, American Statistical Association, vol. 63(1), pages 56-65.
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    More about this item


    collinearity; gretl;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics


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