A New Technique based on Simulations for Improving the Inflation Rate Forecasts in Romania

The necessity of improving the forecasts accuracy grew in the context of actual economic crisis, but few researchers were interested till now in finding out some empirical strategies to improve their predictions. In this article, for the inflation rate forecasts on the horizon 2010-2012, we proved that the one-step-ahead forecasts based on updated AR(2) models could be substantially improved by generating new predictions using Monte Carlo method and bootstrap technique to simulate the models’ coefficients. In this article we introduced a new methodology of constructing the forecasts, by using the limits of the bias-corrected-accelerated bootstrap intervals for the initial data series of the variable to predict. After evaluating the accuracy of the new forecasts, we found out that all the proposed strategies improved the initial AR(2) forecasts and even the predictions of two experts in forecasting. Our own method based on the lower limits of BCA intervals generated the best forecasts. In the forecasting process based on AR models the uncertainty analysis was introduced, by calculating, under the hypothesis of normal distribution, the probability that the predicted value exceeds a critical value.