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LARS: Stata module to perform least angle regression


  • Adrian Mander



Least Angle Regression is a model-building algorithm that considers parsimony as well as prediction accuracy. This method is covered in detail by the paper Efron, Hastie, Johnstone and Tibshirani (2004), published in The Annals of Statistics. Their motivation for this method was a computationally simpler algorithm for the Lasso and Forward Stagewise regression. There are many criticisms of stepwise regression, one of which is that it is a "greedy" algorithm and that the regression coefficients are too large. Ridge regression is one method of model-building that shrinks the coefficients by making the sum of the squared coefficients less than some constant. The Lasso is similar but the constaint is that the sum of the "mod" coefficients is less than a constant. One implication of this will be that the solution will contain coefficients that are exactly 0 and hence have the property of parsimony i.e. a simpler model.

Suggested Citation

  • Adrian Mander, 2006. "LARS: Stata module to perform least angle regression," Statistical Software Components S456860, Boston College Department of Economics, revised 22 Jul 2014.
  • Handle: RePEc:boc:bocode:s456860
    Note: This module should be installed from within Stata by typing "ssc install lars". The module is made available under terms of the GPL v3 ( Windows users should not attempt to download these files with a web browser.

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