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Model Selection in Under-specified Equations Facing Breaks

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  • David Hendry
  • Jennifer L. Castle

Abstract

Although a general unrestricted model may under-specify the data generation process, especially when breaks occur, model selection can still improve over estimating a prior specification. Impulse-indicator saturation (IIS) can 'correct' non-constant intercepts induced by location shifts in omitted variables, which surprisingly leave slope parameters unaltered even when correlated with included variables. However, location shifts in included variables do induce changes in slopes where there are correlated omitted variables. IIS acts as a 'robust method' when models are mis-specified, and helps mitigate the adverse impacts of induced location shifts on non-constant intercepts and equation standard errors.

Suggested Citation

  • David Hendry & Jennifer L. Castle, 2010. "Model Selection in Under-specified Equations Facing Breaks," Economics Series Working Papers 509, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:509
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    6. David Hendry & Carlos Santos, 2010. "An Automatic Test of Super Exogeneity," Economics Series Working Papers 476, University of Oxford, Department of Economics.
    7. Granger,Clive W. J., 1999. "Empirical Modeling in Economics," Cambridge Books, Cambridge University Press, number 9780521662086.
    8. Salkever, David S., 1976. "The use of dummy variables to compute predictions, prediction errors, and confidence intervals," Journal of Econometrics, Elsevier, vol. 4(4), pages 393-397, November.
    9. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    10. David Hendry & Jurgen A. Doornik & Felix Pretis, 2013. "Step-indicator Saturation," Economics Series Working Papers 658, University of Oxford, Department of Economics.
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    12. Carlos Santos & David Hendry & Soren Johansen, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 317-335, April.
    13. Granger, Clive W.J. & Hendry, David F., 2005. "A Dialogue Concerning A New Instrument For Econometric Modeling," Econometric Theory, Cambridge University Press, vol. 21(1), pages 278-297, February.
    14. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
    15. Hendry, David F., 1979. "The behaviour of inconsistent instrumental variables estimators in dynamic systems with autocorrelated errors," Journal of Econometrics, Elsevier, vol. 9(3), pages 295-314, February.
    16. David F. Hendry, 2009. "The Methodology of Empirical Econometric Modeling: Applied Econometrics Through the Looking-Glass," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 1, pages 3-67, Palgrave Macmillan.
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    Cited by:

    1. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    2. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2015. "Robust approaches to forecasting," International Journal of Forecasting, Elsevier, vol. 31(1), pages 99-112.
    3. Calvert Jump, Robert & Kohler, Karsten, 2022. "A history of aggregate demand and supply shocks for the United Kingdom, 1900 to 2016," Explorations in Economic History, Elsevier, vol. 85(C).
    4. Jeyhun I. Mikayilov & Shahriyar Mukhtarov & Hasan Dinçer & Serhat Yüksel & Rıdvan Aydın, 2020. "Elasticity Analysis of Fossil Energy Sources for Sustainable Economies: A Case of Gasoline Consumption in Turkey," Energies, MDPI, vol. 13(3), pages 1-15, February.
    5. Hendry, David F. & Pretis, Felix, 2023. "Analysing differences between scenarios," International Journal of Forecasting, Elsevier, vol. 39(2), pages 754-771.
    6. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    7. David F. Hendry, 2020. "First in, First out: Econometric Modelling of UK Annual CO_2 Emissions, 1860–2017," Economics Papers 2020-W02, Economics Group, Nuffield College, University of Oxford.
    8. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    9. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Felix Pretis, 2015. "Detecting Location Shifts during Model Selection by Step-Indicator Saturation," Econometrics, MDPI, vol. 3(2), pages 1-25, April.
    10. Hendry, David F. & Mizon, Grayham E., 2014. "Unpredictability in economic analysis, econometric modeling and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.
    11. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2013. "Model Selection in Equations with Many ‘Small’ Effects," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 6-22, February.
    12. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2023. "Robust Discovery of Regression Models," Econometrics and Statistics, Elsevier, vol. 26(C), pages 31-51.

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    More about this item

    Keywords

    Model selection; mis-specification; location shifts; impulse-indicator saturation; costs of search; costs of inferencee; Autometrics;
    All these keywords.

    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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