Commenced in January 2007
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Edition: International
Paper Count: 30174
System Identification Based on Stepwise Regression for Dynamic Market Representation

Authors: Alexander Efremov

Abstract:

A system for market identification (SMI) is presented. The resulting representations are multivariable dynamic demand models. The market specifics are analyzed. Appropriate models and identification techniques are chosen. Multivariate static and dynamic models are used to represent the market behavior. The steps of the first stage of SMI, named data preprocessing, are mentioned. Next, the second stage, which is the model estimation, is considered in more details. Stepwise linear regression (SWR) is used to determine the significant cross-effects and the orders of the model polynomials. The estimates of the model parameters are obtained by a numerically stable estimator. Real market data is used to analyze SMI performance. The main conclusion is related to the applicability of multivariate dynamic models for representation of market systems.

Keywords: market identification, dynamic models, stepwise regression.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333804

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References:


[1] P. Deschamps, Exact Small-Sample Inference in Stationary, Fully Regular, Dynamic Demand Models. Journal of Econometrics, Volume 97, pp. 51-91, 2000.
[2] J. Gonzalo and O. Martinez, Large shocks vs. small shocks. (or does size matter? May be so.). Journal of Econometrics, Volume 135, pp. 311-347, 2006.
[3] J. W. Hamister and N. C. Suresh, The impact of pricing policy on sales variability in a supermarket retail context. Int. J. Production Economics, 2007.
[4] D. Hanssens and L. Parsons, Handbooks in Operations Research and Management Science, Volume 5, J. Eliasberg and G. Lilien, Eds. Elsevier Science Publishers, 1993.
[5] S. D. Fassois, MIMO LMS-ARMAX identification of vibrating structures part I: the method. Mechanical Systems and Signal Processing, Volume 15, No 4, pp. 723-735, 2001.
[6] M. Leskensa, L. B. M. Van Kessela and P. M. J. Van den Hof, MIMO closed-loop identification of an MSW incinerator. Control Engineering Practice, Volume 10, pp. 315-326, 2002.
[7] H. Chen, D. Levy, S. Ray and M. Bergen, Asymmetric Price Adjustment in the Small, Journal of Monetary Economics, Volume 55, pp. 728-737, 2008.
[8] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery, Numerical Recipes. The Art of Scientific Computing Third Edition, Cambridge University Press, 2007.
[9] J. O. Rawlings, S. G. Pantula, D. A. Dickey, Applied Regression Analysis: A Research Tool, Second Edition. Springer-Verlag New York, Inc., 1998.
[10] F. Ridder, R. Pintelon, J. Schoukens and D. P. Gillikin, Modified AIC and MDL model selection criteria for short data records. IEEE Trans. on Instrumentation and Measurement, Volume 54, No 1, pp. 144-150, 2005.
[11] L. Ljung, System Identification Toolbox. For Use with Matlab. User-s Guide. The MathWorks, Inc, MA, USA, 2004.
[12] P. Gr¨unwald, The Minimum Description Length Principle. MIT Press, June 2007.
[13] E. Garipov, System Identification. Part II - Identification by Discrete Stochastic Regression Models. Technical University of Sofia, 2004.
[14] M. Verhaegen and V. Verdult, Filtering and System Identification. A Least Squares Approach. Cambridge University Press, 2007.