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Model selection in under-specified equations facing breaks

Author

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  • Castle, Jennifer L.
  • Hendry, David F.

Abstract

When a model under-specifies the data generation process, model selection can improve over estimating a prior specification, especially if location shifts occur. Impulse-indicator saturation (IIS) can ‘correct’ non-constant intercepts induced by location shifts in omitted variables, which leave slope parameters unaltered even when correlated with included variables. Location shifts in included variables induce changes in estimated slopes when there are correlated omitted variables. IIS helps mitigate the adverse impacts of induced location shifts on non-constant intercepts and estimated standard errors, and can provide an automatic intercept correction to improve forecasts following location shifts.

Suggested Citation

  • Castle, Jennifer L. & Hendry, David F., 2014. "Model selection in under-specified equations facing breaks," Journal of Econometrics, Elsevier, vol. 178(P2), pages 286-293.
  • Handle: RePEc:eee:econom:v:178:y:2014:i:p2:p:286-293
    DOI: 10.1016/j.jeconom.2013.08.028
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    References listed on IDEAS

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    1. 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.
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    4. David Hendry & Jurgen A. Doornik & Felix Pretis, 2013. "Step-indicator Saturation," Economics Series Working Papers 658, University of Oxford, Department of Economics.
    5. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
    6. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
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    9. Granger, Clive W.J. & Hendry, David F., 2005. "A Dialogue Concerning A New Instrument For Econometric Modeling," Econometric Theory, Cambridge University Press, vol. 21(01), pages 278-297, February.
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    11. 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.
    12. David Hendry & Carlos Santos, 2010. "An Automatic Test of Super Exogeneity," Economics Series Working Papers 476, University of Oxford, Department of Economics.
    13. Granger,Clive W. J., 1999. "Empirical Modeling in Economics," Cambridge Books, Cambridge University Press, number 9780521778251.
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    Cited by:

    1. 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.
    2. repec:eee:intfor:v:34:y:2018:i:1:p:119-135 is not listed on IDEAS
    3. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Felix Pretis, 2015. "Detecting Location Shifts during Model Selection by Step-Indicator Saturation," Econometrics, MDPI, Open Access Journal, vol. 3(2), pages 1-25, April.
    4. 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.
    5. David Hendry & Grayham E. Mizon, 2016. "Improving the Teaching of Econometrics," Economics Series Working Papers 785, University of Oxford, Department of Economics.
    6. 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.
    7. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.

    More about this item

    Keywords

    Model selection; Mis-specification; Breaks; 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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