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Model Selection in Under-specified Equations Facing Breaks

Author

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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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    File URL: http://www.economics.ox.ac.uk/materials/papers/4640/paper509.pdf
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    References listed on IDEAS

    as
    1. Engle, Robert F. & Hendry, David F., 1993. "Testing superexogeneity and invariance in regression models," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 119-139, March.
    2. 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.
    3. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    4. Christophe Bontemps & Grayham E. Mizon, 2008. "Encompassing: Concepts and Implementation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 721-750, December.
    5. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    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, April.
    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.
    11. 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.
    12. 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.
    13. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
    14. 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.
    15. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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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; location shifts; impulse-indicator saturation; costs of search; costs of inferencee; 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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