Share This Article:

Comparisons of VAR Model and Models Created by Genetic Programming in Consumer Price Index Prediction in Vietnam

Abstract Full-Text HTML Download Download as PDF (Size:841KB) PP. 237-250
DOI: 10.4236/ojs.2012.23029    4,939 Downloads   8,131 Views   Citations
Author(s)    Leave a comment

ABSTRACT

In this paper, we present an application of Genetic Programming (GP) to Vietnamese CPI in?ation one-step prediction problem. This is a new approach in building a good forecasting model, and then applying inflation forecasts in Vietnam in current stage. The study introduces the within-sample and the out-of-samples one-step-ahead forecast errors which have positive correlation and approximate to a linear function with positive slope in prediction models by GP. We also build Vector Autoregression (VAR) model to forecast CPI in quaterly data and compare with the models created by GP. The experimental results show that the Genetic Programming can produce the prediction models having better accuracy than Vector Autoregression models. We have no relavant variables (m2, ex) of monthly data in the VAR model, so no prediction results exist to compare with models created by GP and we just forecast CPI basing on models of GP with previous data of CPI.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

P. Khanh, "Comparisons of VAR Model and Models Created by Genetic Programming in Consumer Price Index Prediction in Vietnam," Open Journal of Statistics, Vol. 2 No. 3, 2012, pp. 237-250. doi: 10.4236/ojs.2012.23029.

References

[1] J. Hamilton, “Time Series Analysis,” Princeton University Press, Princeton, 1994.
[2] C. A. Sims, “Macroeconomics and Reality,” Econometrica, 1980, Vol. 48, No. 1, 1980, pp. 1-48.
[3] J. Koza, “Genetic Programming: On the Programming of Computers by Natural Selection,” MIT Press, Cambridge, 1992.
[4] M. Santini and A. Tettamanzi, “Genetic Programming for ?nancial Time Series Prediction,” Proceedings of Euro Genetic Programming, Lake Como, 18-20 April 2001, pp. 361-370.
[5] D. Rivero, J. R. Rabunal, J. Dorado and A. Pazos, “Time Series Forecast with Anticipation Using Genetic Programming,” 8th International Work-Conference on Arti?cial Neural Networks, Computational Intelligence and Bioinspired Systems, Barcelona, 8-10 June 2005, pp. 968-975.
[6] J. Li, Z. Shi and X. Li, “Genetic Programming with Wavelet-Based Indicators for ?nancial Forecasting,” Transactions of the Institute of Measurement and Control, Vol. 28, No. 3, 2006, pp. 285-297. doi:10.1191/0142331206tim177oa

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.