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Automated Discovery In Econometrics

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  • Phillips, Peter C.B.

Abstract

Our subject is the notion of automated discovery in econometrics. Advances in computer power, electronic communication, and data collection processes have all changed the way econometrics is conducted. These advances have helped to elevate the status of empirical research within the economics profession in recent years, and they now open up new possibilities for empirical econometric practice. Of particular significance is the ability to build econometric models in an automated way according to an algorithm of decision rules that allow for (what we call here) heteroskedastic and autocorrelation robust (HAR) inference. Computerized search algorithms may be implemented to seek out suitable models, thousands of regressions and model evaluations may be performed in seconds, statistical inference may be automated according to the properties of the data, and policy decisions can be made and adjusted in real time with the arrival of new data. We discuss some aspects and implications of these exciting, emergent trends in econometrics.The first version of this paper was written in April 2004 for the 20th Anniversary Issue of Econometric Theory. Helpful comments by the co-editor, Oliver Linton, Benno Pötscher, Brendan Beare, and two referees on the first draft are gratefully acknowledged.

Suggested Citation

  • Phillips, Peter C.B., 2005. "Automated Discovery In Econometrics," Econometric Theory, Cambridge University Press, vol. 21(1), pages 3-20, February.
    • Handle: RePEc:cup:etheor:v:21:y:2005:i:01:p:3-20_05
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    References listed on IDEAS

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    Cited by:

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    2. Bernd Brandl & Christian Keber & Matthias Schuster, 2006. "An automated econometric decision support system: forecasts for foreign exchange trades," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 14(4), pages 401-415, December.
    3. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    4. Jane E. Ihrig & Mario Marazzi & Alexander D. Rothenberg, 2006. "Exchange-rate pass-through in the G-7 countries," International Finance Discussion Papers 851, Board of Governors of the Federal Reserve System (U.S.).
    5. Ziwei Mei & Peter C. B. Phillips & Zhentao Shi, 2022. "The boosted HP filter is more general than you might think," Papers 2209.09810, arXiv.org, revised Apr 2024.
    6. Marquez, Jaime, 2006. "Estimating elasticities for U.S. trade in services," Economic Modelling, Elsevier, vol. 23(2), pages 276-307, March.
    7. Qin, Duo, 2008. "Uncover Latent PPP by Dynamic Factor Error Correction Model (DF-ECM) Approach: Evidence from Five OECD Countries," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 2, pages 1-26.
    8. Jaime R. Marquez, 2005. "Estimating elasticities for U.S. trade in services," International Finance Discussion Papers 836, Board of Governors of the Federal Reserve System (U.S.).
    9. Jennifer L. Castle & Xiaochuan Qin & W. Robert Reed, 2013. "Using Model Selection Algorithms To Obtain Reliable Coefficient Estimates," Journal of Economic Surveys, Wiley Blackwell, vol. 27(2), pages 269-296, April.
    10. Dahl, Christian M. & Hansen, Henrik & Smidt, John, 2009. "The cyclical component factor model," International Journal of Forecasting, Elsevier, vol. 25(1), pages 119-127.
    11. Jungbin Hwang & Gonzalo Valdés, 2020. "Finite-sample Corrected Inference for Two-step GMM in Time Series," Working papers 2020-02, University of Connecticut, Department of Economics.
    12. Heij, C. & Groenen, P.J.F. & van Dijk, D.J.C., 2006. "Time series forecasting by principal covariate regression," Econometric Institute Research Papers EI 2006-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    13. S. Yanki Kalfa & Jaime Marquez, 2021. "Forecasting FOMC Forecasts," Econometrics, MDPI, vol. 9(3), pages 1-21, September.
      • S. Yanki Kalfa & Jaime Marquez, 2018. "Forecasting FOMC Forecasts," Working Papers 2018-007, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    14. Sean Fahle & Jaime R. Marquez & Charles P. Thomas, 2008. "Measuring U.S. international relative prices: a WARP view of the world," International Finance Discussion Papers 917, Board of Governors of the Federal Reserve System (U.S.).
    15. Rustam Ibragimov & Paul Kattuman & Anton Skrobotov, 2021. "Robust Inference on Income Inequality: $t$-Statistic Based Approaches," Papers 2105.05335, arXiv.org, revised Nov 2021.
    16. Sun, Yixiao & Yang, Jingjing, 2020. "Testing-optimal kernel choice in HAR inference," Journal of Econometrics, Elsevier, vol. 219(1), pages 123-136.
    17. S. Yanki Kalfa & Jaime Marquez, 2019. "FOMC Forecasts: Are They Useful for Understanding Monetary Policy?," JRFM, MDPI, vol. 12(3), pages 1-17, August.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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