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.
Highlights
Inflation has great importance to saving decisions, investment, interest rate, production and consumption
We present an application of Genetic Programming (GP) to Vietnamese CPI inflation one-step prediction problem
We have no relavant variables (m2, ex) of monthly data in the Vector Autoregression (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
Summary
Inflation has great importance to saving decisions, investment, interest rate, production and consumption. Decisions basing on impractical inflation predictions result in uneffective resource allocation and weaker macroeconomic activities. Better predictions see better forecast solutions given by economic agents and improve the entire economic performance. Different models depending on the theory of different price fixation are often used for describing inflation evolutions. These models emphasize the role of different variables in inflation. The different econometric models have different modeling specification, and information quality. Despite of huge explained variables in models basing on theory to improve the level of conformity, they uncertainly ameliorate the ability of prediction models
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