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Linear Regression Diagnostics

Author

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  • Roy E. Welsch
  • Edwin Kuh

Abstract

This paper attempts to provide the user of linear multiple regression with a battery of diagnostic tools to determine which, if any, data points have high leverage or influence on the estimation process and how these possibly discrepant data points differ from the patterns set by the majority of the data. The point of view taken is that when diagnostics indicate the presence of anomolous data, the choice is open as to whether these data are in fact unusual and helpful, or possibly harmful and thus in need of modifications or deletion. The methodology developed depends on differences, derivatives, and decompositions of basic regression statistics. There is also a discussion of how these techniques can be used with robust and ridge estimators. An example is given showing the use of diagnostic methods in the estimation of a cross-country savings rate model.

Suggested Citation

  • Roy E. Welsch & Edwin Kuh, 1977. "Linear Regression Diagnostics," NBER Working Papers 0173, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:0173
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    References listed on IDEAS

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    1. David A. Belsley, 1976. "Multicollinearity: Diagnosing its Presence and Assessing the Potential Damage It Causes Least Squares Estimation," NBER Working Papers 0154, National Bureau of Economic Research, Inc.
    2. Fisher, Franklin M, 1970. "Tests of Equality Between Sets of Coefficients in Two Linear Regressions: An Expository Note," Econometrica, Econometric Society, vol. 38(2), pages 361-366, March.
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    Cited by:

    1. Xu-Ping Zhong & Bo-Cheng Wei & Wing-Kam Fung, 2000. "Influence Analysis for Linear Measurement Error Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(2), pages 367-379, June.
    2. Torfinn Harding & Beata S. Javorcik, 2011. "Roll Out the Red Carpet and They Will Come: Investment Promotion and FDI Inflows," Economic Journal, Royal Economic Society, vol. 121(557), pages 1445-1476, December.
    3. John C. Bluedorn & Christopher Bowdler & Christoffer Koch, 2017. "Heterogeneous Bank Lending Responses to Monetary Policy: New Evidence from a Real-Time Identification," International Journal of Central Banking, International Journal of Central Banking, vol. 13(1), pages 95-149, February.
    4. Filipe R. Campante & Davin Chor & Quoc-Anh Do, 2009. "Instability And The Incentives For Corruption," Economics and Politics, Wiley Blackwell, vol. 21(1), pages 42-92, March.
    5. Isabelle Bensidoun & Sébastien Jean & Aude Sztulman, 2011. "International trade and income distribution: reconsidering the evidence," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 147(4), pages 593-619, November.
    6. Kontonikas, Alexandros & MacDonald, Ronald & Saggu, Aman, 2013. "Stock market reaction to fed funds rate surprises: State dependence and the financial crisis," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4025-4037.
    7. Berno Buechel & Lydia Mechtenberg & Julia Petersen, 2014. "Peer Effects and Students’ Self-Control," SFB 649 Discussion Papers SFB649DP2014-024, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    8. Ezcurra, Roberto & Rodríguez-Pose, Andrés, 2013. "Does Economic Globalization affect Regional Inequality? A Cross-country Analysis," World Development, Elsevier, vol. 52(C), pages 92-103.
    9. Bordo, Michael D. & Duca, John V. & Koch, Christoffer, 2016. "Economic policy uncertainty and the credit channel: Aggregate and bank level U.S. evidence over several decades," Journal of Financial Stability, Elsevier, vol. 26(C), pages 90-106.
    10. Chava, Sudheer & Purnanandam, Amiyatosh, 2011. "The effect of banking crisis on bank-dependent borrowers," Journal of Financial Economics, Elsevier, vol. 99(1), pages 116-135, January.
    11. Jensen, D. R., 2001. "Properties of selected subset diagnostics in regression," Statistics & Probability Letters, Elsevier, vol. 51(4), pages 377-388, February.
    12. Roberto Franzosi, 1994. "Outside and inside the regression “black box” from exploratory to interior data analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 28(1), pages 21-53, February.
    13. Koch, Christoffer, 2015. "Deposit interest rate ceilings as credit supply shifters: Bank level evidence on the effects of Regulation Q," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 316-326.
    14. Fung, Wing K., 1995. "A cautionary note on the use of generalized Cook-type measures," Computational Statistics & Data Analysis, Elsevier, vol. 19(3), pages 321-326, March.
    15. Yoon, Jangho, 2011. "Effect of increased private share of inpatient psychiatric resources on jail population growth: Evidence from the United States," Social Science & Medicine, Elsevier, vol. 72(4), pages 447-455, February.
    16. Buechel, Berno & Mechtenberg, Lydia & Petersen, Julia, 2014. "Peer Effects and Students’ Self-Control," MPRA Paper 53658, University Library of Munich, Germany.
    17. Calì, Massimiliano & te Velde, Dirk Willem, 2011. "Does Aid for Trade Really Improve Trade Performance?," World Development, Elsevier, vol. 39(5), pages 725-740, May.
    18. Juan Carlos Cuestas & Maurizio Intartaglia, 2016. "Do institutions alleviate poverty? New Empirical Evidence," Economics Bulletin, AccessEcon, vol. 36(1), pages 145-154.
    19. repec:eee:energy:v:127:y:2017:i:c:p:479-488 is not listed on IDEAS
    20. Ithai Stern & Sharon D. James, 2016. "Whom are you promoting? Positive voluntary public disclosures and executive turnover," Strategic Management Journal, Wiley Blackwell, vol. 37(7), pages 1413-1430, July.

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