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Using linear regression to establish empirical relationships

Author

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  • Marno Verbeek

    (Erasmus University, the Netherlands)

Abstract

Linear regression is a powerful tool for investigating the relationships between multiple variables by relating one variable to a set of variables. It can identify the effect of one variable while adjusting for other observable differences. For example, it can analyze how wages relate to gender, after controlling for differences in background characteristics such as education and experience. A linear regression model is typically estimated by ordinary least squares, which minimizes the differences between the observed sample values and the fitted values from the model. Multiple tools are available to evaluate the model.

Suggested Citation

  • Marno Verbeek, 2017. "Using linear regression to establish empirical relationships," IZA World of Labor, Institute of Labor Economics (IZA), pages 336-336, February.
    • Handle: RePEc:iza:izawol:journl:y:2017:n:336
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    References listed on IDEAS

    as
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    7. Mitchell A. Petersen, 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 435-480, January.
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    Cited by:

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    More about this item

    Keywords

    linear regression; ordinary least squares; model specification; estimation and inference; causality;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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