System Identification Based on Stepwise Regression for Dynamic Market Representation
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:56519", author = "Alexander Efremov", title = "System Identification Based on Stepwise Regression for Dynamic Market Representation", 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.", volume = "4", number = "4", pages = "736-6", }