Stochastic models for forecasting inflation rate. Empirical evidence from Romania

The time series can be modeled by stochastic processes which are intended to explain the manner of economic phenomena evolution. Depending on the type of time series, several categories of stochastic processes as models for time series are used: autoregressive processes, moving average processes and composite models based on them. In general, each model has its own advantages and disadvantages, the aim of this study is to distinguish and to identify the most important features of each to determine which model provides the best predictions. In the empirical analysis we considered time series consists of data taken from the monthly reports of the National Bank of Romania for the inflation rate, between January 1997 - August 2013. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.