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Model Discovery and Trygve Haavelmo's Legacy

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  • David Hendry
  • Soren Johansen

Abstract

Trygve Haavelmo's Probability Approach aimed to implement economic theories, but he later recognized their incompleteness. Although he did not explicitly consider model selection, we apply it when theory-relevant variables, {xt}, are retained without selection while selecting other candidate variables, {wt}. Under the null that the {wt} are irrelevant, by orthogonalizing with respect to the {xt}, the estimator distributions of the xt's parameters are unaffected by selection even for more variables than observations and for endogenous variables. Under the alternative, when the joint model nests the generating process, an improved outcome results from selection. This implements Haavelmo's program relatively costlessly.

Suggested Citation

  • David Hendry & Soren Johansen, 2012. "Model Discovery and Trygve Haavelmo's Legacy," Economics Series Working Papers 598, University of Oxford, Department of Economics.
  • Handle: RePEc:oxf:wpaper:598
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    File URL: http://www.economics.ox.ac.uk/materials/papers/5732/paper598.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Felix Pretis & Lea Schneider & Jason E. Smerdon & David F. Hendry, 2016. "Detecting Volcanic Eruptions In Temperature Reconstructions By Designed Break-Indicator Saturation," Journal of Economic Surveys, Wiley Blackwell, vol. 30(3), pages 403-429, July.
    2. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    3. Ericsson, Neil R., 2017. "How biased are U.S. government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 543-559.
    4. Rocha, Jordano Vieira & Pereira, Pedro L. Valls, 2015. "Forecast comparison with nonlinear methods for Brazilian industrial production," Textos para discussão 397, FGV/EESP - Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
    5. David Hendry & John Muellbauer, 2017. "The future of macroeconomics: Macro theory and models at the Bank of England," Economics Series Working Papers 832, University of Oxford, Department of Economics.
    6. Ericsson, Neil R., 2016. "Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis," International Journal of Forecasting, Elsevier, vol. 32(2), pages 571-583.
    7. repec:eee:intfor:v:34:y:2018:i:1:p:119-135 is not listed on IDEAS
    8. Muellbauer, John, 2016. "Macroeconomics and Consumption," CEPR Discussion Papers 11588, C.E.P.R. Discussion Papers.
    9. Jennifer Castle & David Hendry, 2016. "Policy Analysis, Forediction, and Forecast Failure," Economics Series Working Papers 809, University of Oxford, Department of Economics.
    10. Jurgen A. Doornik & David F. Hendry & Steve Cook, 2015. "Statistical model selection with “Big Data”," Cogent Economics & Finance, Taylor & Francis Journals, vol. 3(1), pages 1045216-104, December.
    11. repec:taf:oaefxx:v:4:y:2016:i:1:p:1170096 is not listed on IDEAS
    12. repec:gam:jecnmx:v:5:y:2017:i:3:p:39-:d:110547 is not listed on IDEAS
    13. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    14. Biørn, Erik, 2017. "Identification, Instruments, Omitted Variables, and Rudimentary Models: Fallacies in the ‘Experimental Approach’ to Econometrics," Memorandum 13/2017, Oslo University, Department of Economics.
    15. Nymoen, Ragnar & Pedersen, Kari & Sjåberg, Jon Ivar, 2018. "Estimation of effects of recent macroprudential policies in a sample of advanced open economies," Memorandum 5/2018, Oslo University, Department of Economics.
    16. David Hendry & Grayham E. Mizon, 2016. "Improving the Teaching of Econometrics," Economics Series Working Papers 785, University of Oxford, Department of Economics.
    17. Ericsson Neil R., 2016. "Testing for and estimating structural breaks and other nonlinearities in a dynamic monetary sector," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 377-398, September.

    More about this item

    Keywords

    Trygve Haavelmo; Model discovery; Theory retention; Impulse-indicator saturation; Autometrics;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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