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General-to-specific modeling in Stata

Author

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  • Damian Clarke

    (Department of Economics, University of Oxford)

Abstract

Empirical researchers are frequently confronted with issues regarding which explanatory variables to include in their models. This article describes the application of a well-known model-selection algorithm to Stata: general-to-specific (GETS) modeling. This process provides a prescriptive and defendable way of selecting a few relevant variables from a large list of potentially important variables when fitting a regression model. Several empirical issues in GETS modeling are then discussed, specifically, how such an algorithm can be applied to estimations based upon cross-sectional, time-series, and panel data. A command is presented, written in Stata and Mata, that implements this algorithm for various data types in a flexible way. This command is based on Stata’s regress or xtreg command, so it is suitable for researchers in the broad range of fields where regression analysis is used. Finally, the genspec command is illustrated using data from applied studies of GETS modeling with Monte Carlo simulation. It is shown to perform as empirically predicted and to have good size and power (or gauge and potency) properties under simulation. Copyright 2014 by StataCorp LP.

Suggested Citation

  • Damian Clarke, 2014. "General-to-specific modeling in Stata," Stata Journal, StataCorp LP, vol. 14(4), pages 895-908, December.
    • Handle: RePEc:tsj:stataj:v:14:y:2014:i:4:p:895-908
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    Cited by:

    1. Blaise Gnimassoun, Joseph Keneck Massil, 2019. "Determinants of corruption: can we put all countries in the same basket?," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 16(2), pages 239-276, December.
    2. Blaise Gnimassoun, 2017. "Exchange rate misalignments and the external balance under a pegged currency system," Review of International Economics, Wiley Blackwell, vol. 25(5), pages 949-974, November.
    3. Gutmann, Jerg & Padovano, Fabio & Voigt, Stefan, 2020. "Perception vs. experience: Explaining differences in corruption measures using microdata," European Journal of Political Economy, Elsevier, vol. 65(C).
    4. Jiří Gregor & Aleš Melecký & Martin Melecký, 2021. "Interest Rate Pass‐Through: A Meta‐Analysis Of The Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 35(1), pages 141-191, February.
    5. Blaise Gnimassoun & Joseph Keneck Massil, 2016. "Determinants of corruption: Can we put all countries in the same basket?," Working Papers hal-04141599, HAL.
    6. Gnimassoun, Blaise, 2015. "The importance of the exchange rate regime in limiting current account imbalances in sub-Saharan African countries," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 36-74.
    7. Barry Eichengreen, 2023. "Jackson Hole 2023 - Global Financial Flows," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, August.
    8. Ramona Jimborean & Anna Kelber, 2017. "Foreign Direct Investment Drivers and Growth in Central and Eastern Europe in the Aftermath of the 2007 Global Financial Crisis," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 59(1), pages 23-54, March.
    9. Lucas Menescal & José Alves, 2023. "Tax structure and public sector efficiency: new evidence for developing countries," Working Papers REM 2023/0291, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    10. Blaise Gnimassoun, 2015. "Exchange rate misalignments and the external balance under a pegged currency system," Working Papers hal-04141421, HAL.
    11. Ewa Cukrowska-Torzewska & Anna Matysiak, 2018. "The Motherhood Wage Penalty: A Meta-Analysis," VID Working Papers 1808, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna.

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