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Balanced Variable Addition In Linear Models

Author

Listed:
  • Giuseppe De Luca
  • Jan R. Magnus
  • Franco Peracchi

Abstract

This paper studies what happens when we move from a short regression to a long regression in a setting where both regressions are subject to misspecification. In this setup, the least‐squares estimator in the long regression may have larger inconsistency than the least‐squares estimator in the short regression. We provide a simple interpretation for the comparison of the inconsistencies and study under which conditions the additional regressors in the long regression represent a “balanced addition” to the short regression.

Suggested Citation

  • Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2018. "Balanced Variable Addition In Linear Models," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 1183-1200, September.
    • Handle: RePEc:bla:jecsur:v:32:y:2018:i:4:p:1183-1200
    DOI: 10.1111/joes.12245
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    References listed on IDEAS

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    7. Jan R. Magnus & Giuseppe De Luca, 2016. "Weighted-Average Least Squares (Wals): A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 117-148, February.
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    Cited by:

    1. Deepankar Basu, 2018. "Bias of OLS Estimators due to Exclusion of Relevant Variables and Inclusion of Irrelevant Variables," UMASS Amherst Economics Working Papers 2018-19, University of Massachusetts Amherst, Department of Economics.
    2. Hai-Anh H. Dang & Talip Kilic & Ksenia Abanokova & Gero Carletto, 2024. "Imputing Poverty Indicators without Consumption Data : An Exploratory Analysis," Policy Research Working Paper Series 10867, The World Bank.
    3. Hai‐Anh H. Dang & Talip Kilic & Kseniya Abanokova & Calogero Carletto, 2025. "Poverty Imputation in Contexts Without Consumption Data: A Revisit With Further Refinements," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 71(1), February.
    4. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2019. "Comments on “Unobservable Selection and Coefficient Stability: Theory and Evidence” and “Poorly Measured Confounders are More Useful on the Left Than on the Right”," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 217-222, April.
    5. Hai-Anh H. Dang & Paolo Verme, 2023. "Estimating poverty for refugees in data-scarce contexts: an application of cross-survey imputation," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 653-679, April.
    6. Hai‐Anh H. Dang & Peter F. Lanjouw, 2023. "Measuring Poverty Dynamics with Synthetic Panels Based on Repeated Cross Sections," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 599-622, June.
    7. Theresa Beltramo & Hai-Anh Dang & Ibrahima Sarr & Paolo Verme, 2024. "Estimating poverty among refugee populations: a cross-survey imputation exercise for Chad," Oxford Development Studies, Taylor & Francis Journals, vol. 52(1), pages 94-113, January.
    8. Dang, Hai-Anh H. & Verme, Paolo, 2019. "Estimating Poverty for Refugee Populations: Can Cross-Survey Imputation Methods Substitute for Data Scarcity?," GLO Discussion Paper Series 429, Global Labor Organization (GLO).
    9. Deepankar Basu, 2023. "Formal Covariate Benchmarking to Bound Omitted Variable Bias," Papers 2306.10562, arXiv.org.
    10. Jan R. Magnus, 2019. "On Using the t -Ratio as a Diagnostic," Econometrics, MDPI, vol. 7(2), pages 1-3, May.

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