Computer Automation of General-to-Specific Model Selection Procedures
AbstractOver 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.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 314.
Date of creation: 01 Mar 1999
Date of revision:
Other versions of this item:
- Krolzig, Hans-Martin & Hendry, David F., 2001. "Computer automation of general-to-specific model selection procedures," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 831-866, June.
- David Hendry & Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Economics Series Working Papers 3, University of Oxford, Department of Economics.
- Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Econometric Society World Congress 2000 Contributed Papers 0411, Econometric Society.
- 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 &bull Diffusion Processes
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Sawa, Takamitsu, 1978. "Information Criteria for Discriminating among Alternative Regression Models," Econometrica, Econometric Society, vol. 46(6), pages 1273-91, November.
- Kevin Hoover & Stephen J. Perez, 2003.
"Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search,"
9727, University of California, Davis, Department of Economics.
- 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.
- Kevin D. Hoover & Stephen J. Perez, . "Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search," Department of Economics 97-27, California Davis - Department of Economics.
- Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164, September.
- Nicholls, D F & Pagan, A R, 1983. "Heteroscedasticity in Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 51(4), pages 1233-42, July.
- Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
- 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 1303-10, November.
- 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.
- David F. Hendry & Neil R. Ericsson, 1990.
"Modeling the demand for narrow money in the United Kingdom and the United States,"
International Finance Discussion Papers
383, Board of Governors of the Federal Reserve System (U.S.).
- 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.
- David F. Hendry & Neil R. Ericsson, 1989. "An econometric analysis of UK money demand in MONETARY TRENDS IN THE UNITED STATES AND THE UNITED KINGDOM by Milton Friedman and Anna J. Schwartz," International Finance Discussion Papers 355, Board of Governors of the Federal Reserve System (U.S.).
- Hendry, David F, 1980. "Econometrics-Alchemy or Science?," Economica, London School of Economics and Political Science, vol. 47(188), pages 387-406, November.
- 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.
- 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 661-92, December.
- Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, December.
- 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 113-44, January.
- White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
- 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.
- 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.
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