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Variable selection in linear regression

Author

Listed:
  • Charles Lindsey

    () (StataCorp)

  • Simon Sheather

    (Texas A&M University)

Abstract

We present a new Stata program, vselect, that helps users perform variable selection after performing a linear regression. Options for stepwise meth- ods such as forward selection and backward elimination are provided. The user may specify Mallows’s Cp, Akaike’s information criterion, Akaike’s corrected informa- tion criterion, Bayesian information criterion, or R2 adjusted as the information criterion for the selection. When the user specifies the best subset option, the leaps-and-bounds algorithm (Furnival and Wilson, Technometrics 16: 499–511) is used to determine the best subsets of each predictor size. All the previously men- tioned information criteria are reported for each of these subsets. We also provide options for doing variable selection only on certain predictors (as in [R] nestreg) and support for weighted linear regression. All options are demonstrated on real datasets with varying numbers of predictors.

Suggested Citation

  • Charles Lindsey & Simon Sheather, 2010. "Variable selection in linear regression," Stata Journal, StataCorp LP, vol. 10(4), pages 650-669, December.
  • Handle: RePEc:tsj:stataj:v:10:y:2010:i:4:p:650-669 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj10-4/st0213/
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    References listed on IDEAS

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    4. John M. Abowd & Francis Kramarz & David N. Margolis, 1999. "High Wage Workers and High Wage Firms," Econometrica, Econometric Society, vol. 67(2), pages 251-334, March.
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    6. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 225-238.
    7. Anabela Carneiro & Paulo Guimarães & Pedro Portugal, 2012. "Real Wages and the Business Cycle: Accounting for Worker, Firm, and Job Title Heterogeneity," American Economic Journal: Macroeconomics, American Economic Association, vol. 4(2), pages 133-152, April.
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    Cited by:

    1. Lee, Gi-Eu, 2016. "Temperature Effects are more Complex than Degrees: A Case Study on Residential Energy Consumption," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 242285, Agricultural and Applied Economics Association.
    2. Agarwal, Vikas & Arisoy, Y. Eser & Naik, Narayan Y., 2017. "Volatility of aggregate volatility and hedge fund returns," Journal of Financial Economics, Elsevier, vol. 125(3), pages 491-510.
    3. Butler, Alexander W. & Keefe, Michael O'Connor & Kieschnick, Robert, 2014. "Robust determinants of IPO underpricing and their implications for IPO research," Journal of Corporate Finance, Elsevier, vol. 27(C), pages 367-383.
    4. Henrike Junge, 2017. "From Gross to Net Wages in German Administrative Data Sets," Data Documentation 89, DIW Berlin, German Institute for Economic Research.
    5. Amin, Ariane, 2016. "Exploring the role of economic incentives and spillover effects in biodiversity conservation policies in sub-Saharan Africa," Ecological Economics, Elsevier, vol. 127(C), pages 185-191.
    6. repec:ags:aaea16:235739 is not listed on IDEAS
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    8. Anwar, Sajid & Sun, Sizhong, 2012. "Trade liberalisation, market competition and wage inequality in China's manufacturing sector," Economic Modelling, Elsevier, vol. 29(4), pages 1268-1277.
    9. Valentino Dardanoni & Giuseppe De Luca & Salvatore Modica & Franco Peracchi, 2012. "A generalized missing-indicator approach to regression with imputed covariates," Stata Journal, StataCorp LP, vol. 12(4), pages 575-604, December.
    10. Wenming Xu & Guangdong Xu, 2016. "Truth and Robustness in Cross-country Law and Finance Regressions: A Bayesian analysis of the Empirical “Law Matters†Thesis," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 6(6), pages 1-6.

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    Keywords

    vselect; variable selection; regress; nestreg;

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