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In Iran, one of the most important economic problems in recent decades is the phenomenon of inflation. Achieving a stable inflation rate requires the ability to use efficient and effective tools in economic policy-making. Hence, economic policymakers should have a proper understanding of the effects of the policies applied and be able to adjust their economic instruments with precise inflation forecasts. EWS has been designed and to anticipate inflationary crises and anticipate an impending incident based on signs that appear on the economy before a crisis happens. In this paper, the behavior of inflation rate has been investigated with BDS and maximum Lyapunov exponent tests, with the help of Eviews and MATLAB. If the time series of the inflation rate is non-random, a definite nonlinear function can analyze the behavior of the series with the least error. This study was intended to design a comprehensive early warning system for inflation in the country. In this regard, using the inflation rate, the critical points of the Iranian economy between the years 1990 to 2016 were identified and classified. Then, a well-designed Markov switching autoregressive model was used. The results showed that, it takes 1 to 2 periods on average for the high inflationary periods and 10 periods on average for the low inflationary periods to change direction.

Chaotic Analysis and Design of an Early Warning System for Inflation in Iran Using Markov Switching Autoregressive Approach