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Computer Automation of General-to-Specific Model Selection Procedures

Author

Listed:
  • Hans-Martin Krolzig

    () (University of Oxford)

  • David Hendry

    () (Nuffield College, Oxford)

Abstract

Over the last three decades, the LSE methodology (see Hendry, 1993, for an overview) has emerged as a leading approach for pursuing econometrics. One of its main tenets is the concept of general-to-specific modelling: Starting from a general dynamic statistical model, which captures the essential characteristics of the underlying data set, standard testing procedures are used to reduce its complexity by eliminating statistically insignificant variables and to check the validity of the reductions in order to ensure the congruency of the model. As the reduction process is inherently iterative, many reduction paths can be considered, which may lead to different terminal specifications. Encompassing is then used to test between these, usually non-nested, specifications, and only models which survive the encompassing step are kept for further consideration. If more than one model survives the "testimation" process, it becomes the new general model, and the specification process is re-applied to it. This paper proposes a computer automation of the general-to-specific model-selection process, which we call PcGets (GEneral-To-Specific). Written in Ox (see Doornik, 1998), it is a package designed for general-to-specific modelling of economic processes. In Monte Carlo experiments, the general-to-specific approach of PcGets recovers the specification of the DGP with a remarkable accuracy. The empirical size and power of the specification found by PcGets are investigated and found to be as one would expect if the DGP were known.

Suggested Citation

  • Hans-Martin Krolzig & David Hendry, 1999. "Computer Automation of General-to-Specific Model Selection Procedures," Computing in Economics and Finance 1999 314, Society for Computational Economics.
  • Handle: RePEc:sce:scecf9:314
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    References listed on IDEAS

    as
    1. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
    2. Godfrey, Leslie G, 1978. "Testing for Higher Order Serial Correlation in Regression Equations When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1303-1310, November.
    3. Hendry, David F & Ericsson, Neil R, 1991. "An Econometric Analysis of U.K. Money Demand in 'Monetary Trends in the United States and the United Kingdom' by Milton Friedman and Anna Schwartz," American Economic Review, American Economic Association, vol. 81(1), pages 8-38, March.
    4. Hendry, David F & Doornik, Jurgen A, 1994. "Modelling Linear Dynamic Econometric Systems," Scottish Journal of Political Economy, Scottish Economic Society, vol. 41(1), pages 1-33, February.
    5. Hendry, David F. & Ericsson, Neil R., 1991. "Modeling the demand for narrow money in the United Kingdom and the United States," European Economic Review, Elsevier, vol. 35(4), pages 833-881, May.
    6. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
    7. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    8. Nicholls, D F & Pagan, A R, 1983. "Heteroscedasticity in Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 51(4), pages 1233-1242, July.
    9. Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, February.
    10. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    11. David F. Hendry & Hans-Martin Krolzig, 1999. "Improving on 'Data mining reconsidered' by K.D. Hoover and S.J. Perez," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 202-219.
    12. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
    13. Hendry, David F., 1984. "Monte carlo experimentation in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 16, pages 937-976, Elsevier.
    14. Hendry, David F, 1980. "Econometrics-Alchemy or Science?," Economica, London School of Economics and Political Science, vol. 47(188), pages 387-406, November.
    15. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    16. Davidson, James E H, et al, 1978. "Econometric Modelling of the Aggregate Time-Series Relationship between Consumers' Expenditure and Income in the United Kingdom," Economic Journal, Royal Economic Society, vol. 88(352), pages 661-692, December.
    17. Sawa, Takamitsu, 1978. "Information Criteria for Discriminating among Alternative Regression Models," Econometrica, Econometric Society, vol. 46(6), pages 1273-1291, November.
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    More about this item

    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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