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Regression Anatomy, Revealed

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  • Filoso, Valerio

Abstract

This paper presents a command, reganat, which implements graph ically the method of regression anatomy as described by Angrist and Pischke (2009). This tool can help the analyst in the validation of linear models, since it produces a bi-dimensional scatterplot obtained under the control of other covariates.

Suggested Citation

  • Filoso, Valerio, 2010. "Regression Anatomy, Revealed," MPRA Paper 23224, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:23224
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    References listed on IDEAS

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    1. James Feyrer & Bruce Sacerdote & Ariel Dora Stern, 2008. "Will the Stork Return to Europe and Japan? Understanding Fertility within Developed Nations," Journal of Economic Perspectives, American Economic Association, vol. 22(3), pages 3-22, Summer.
    2. Ruud, Paul A., 2000. "An Introduction to Classical Econometric Theory," OUP Catalogue, Oxford University Press, number 9780195111644, Decembrie.
    3. Michael C. Lovell, 1963. "Seasonal Adjustment of Economic Time Series and Multiple Regression," Cowles Foundation Discussion Papers 151, Cowles Foundation for Research in Economics, Yale University.
    4. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    5. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, Decembrie.
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    Cited by:

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    More about this item

    Keywords

    Regression anatomy; Frisch-Waugh-Lovell theorem; Multivariate Regression Models;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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