A Discrete Time Markov Chain Model using Maximum Likelihood for the Assessment of Inflation Rate in Pakistan

Markov chains represent a class of stochastic processes with wide-ranging applications. This study employs discrete time Markov chains (DTMC) to model transition probabilities between discrete inflation states using matrix methods. Inflation data from July 2000 to April 2015 from the State Bank of Pakistan were categorized into three states: Deflation (rate ≤ 0), Creep (rate ≤ 1), and Normal (rate > 1). A simulation technique generated random sequences of inflation states, and maximum likelihood estimation was used to derive model parameters. The equilibrium distribution was computed to verify model stability and forecast long-term inflation behavior.