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A flexible tool for model building: the relevant transformation of the imputs network approach

Pérez Amaral, Teodosio and Gallo, Giampiero M. and White, Halbert (2003) A flexible tool for model building: the relevant transformation of the imputs network approach. [Working Paper or Technical Report]

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Abstract

A new method, called Relevant Transformation of the Inputs Network Approach (RETINA) is proposed as a tool for model building. It is designed around flexibility (with nonlinear transformations of the predictors of interest), selective search within the range of possible models, out-of-sample forecasting ability and computational simplicity. In tests on simulated data, it shows both a high rate of successful retrieval of the DGP which increases with the sample size and a good performance relative to other alternative procedures. A telephone service demand model is built to show how the procedure applies on real data.

Item Type:Working Paper or Technical Report
Uncontrolled Keywords:Relevant Transformation of the Inputs Network Approach (RETINA). Economics models
Subjects:Social sciences > Economics > Econometrics
Series Name:UCM. Instituto Complutense de Análisis Económico. Documentos de trabajo
Volume:2003
Number:0309
ID Code:7689
References:

Akaike, H. (1973). ‘Information theory and an extension of the likelihood principle’, in B.N. Petrov and F. Csaki (eds.), Proceedings of the Second International Symposium on Information Theory. Budapest: Akademiai Kiado, 267-281.

Breiman, L. (1995). ‘Better subset regression using the nonnegative garrote’, Technometrics, Vol. 37, 373-84.

Burnham, K. and Anderson, D. (2002). Model Selection and Inference: A Practical Information-Theoretic Approach, 2nd ed., Springer-Verlag, New York.

Campos J. and Ericsson, N.R. (1999). ‘Constructive data mining: modeling consumers’ expenditure in Venezuela’, Econometrics Journal, Vol. 2, 226-40.

Campos J., Ericsson, N.R., and D.F. Hendry (2003). Readings in General-to-pecific Modeling, Edward Elgar, Cheltenham, forthcoming.

Diebold, F.X. and Mariano, R.S. (1995), Comparing predictive accuracy, Journal of Business and Economic Statistics, Vol. 13, 253-63.

Giacomini, R. and White, H. (2003), ‘Tests of conditional predictive ability’, UCSD Dept. of Economics, Working Paper 2003-09.

Granger, C.W.J., King, M. and White, H. (1995). ‘Comments on testing economic theories and the use of model selection criteria’, Journal of Econometrics, Vol. 67, 173-87.

Granger, C.W.J. and Newbold P. (1973). ‘Evaluation of forecasts’, Applied Economics, Vol. 5, 35-47.

Granger, C.W.J. and Timmermann, A. (1999). ‘Data mining with local model specification uncertainty: a discussion of Hoover and Perez’, Econometrics Journal, Vol. 2, 220-25.

Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models, Monographs on Statistics and Applied Probability 43, Chapman and Hall, London.

Hendry, D.F. and Krolzig, H.-M. (1999). ‘Improving on ‘Data mining reconsidered’ by K.D. Hoover and S.J. Perez’, Econometrics Journal, Vol. 2, 202-19.

Hendry, D.F. and Krolzig, H.-M. (2003a). ‘New developments in automatic general-tospecific modeling’, forthcoming in B.P. Stigum (ed.), Econometrics and the Philosophy of Economics, Princeton University Press, Princeton.

Hendry, D.F. and Krolzig, H.-M. (2003b). ‘Sub-sample model selection procedures in Gets modelling’, forthcoming in R. Becker and S. Hurn (eds.), Advances in Economics and Econometrics: Theory and Applications, Edward Elgar, Cheltenham.

Hoover, K.D. and Perez, S.J. (1999). ‘Data mining reconsidered: encompassing and the general-to-specific approach to specification search’, Econometrics Journal, Vol. 2, 167-91.

Inoue, A. and Kilian, L. (2003). ‘On the selection of forecasting models’, WP 214, European Central Bank.

Krolzig, H.-M. and D.F. Hendry (2001). ‘Computer automation of general-to-specific model selection procedures’, Journal of Economic Dynamics and Control, 25, 831-66.

Miller, A. J. (1990). Subset Selection in Regression, Monographs on Statistics and Applied Probability 40, Chapman and Hall, London.

Pérez-Amaral, T. and Marinucci, M. (2002), ‘Econometric modeling of business telephone toll demand for individual firms using a new model selection approach, RETINA’, presented at the 13th Regional Conference of the international Telecommunications Society, Madrid.

Schwartz, G. (1978). ‘Estimating the dimension of a model’, Annals of Statistics, Vol. 6, 461-64.

Sin, C.-Y. and White, H. (1996), ‘Information criteria for selecting possibly misspecified parametric models’, Journal of Econometrics, Vol. 71, 207-25.

Taylor, L. D. (1994). Telecommunications Demand Modelling: Theory and Applications, Dordrecht, Kluwer.

White, H., (1989). ‘Learning in artificial neural networks: a statistical perspective’, Neural Computation, Vol. 1, 425-64, (reprinted in White, H. (1992). Artificial Neural Networks: Approximation and Learning Theory Oxford, Blackwell).

White, H. (1998). ‘Artificial neural network and alternative methods for assessing naval readiness’. Technical Report, NRDA, San Diego.

White, H. (2000). ‘A reality check for data snooping’, Econometrica, 68, 1097-1126.

West, K. D. (1996). ‘Asymptotic inference about predictive ability’, Econometrica, 64, 1067–84.

Deposited On:10 Mar 2008
Last Modified:06 Feb 2014 07:55

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