TITLE:
Comparisons of VAR Model and Models Created by Genetic Programming in Consumer Price Index Prediction in Vietnam
AUTHORS:
Pham Van Khanh
KEYWORDS:
Vector Autoregression; Genetic Programming; CPI Inflation; Forecast
JOURNAL NAME:
Open Journal of Statistics,
Vol.2 No.3,
July
10,
2012
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.