Predicting the Value of Agricultural GDP in Iraq for the Period 2019—2030 by Applying the Markov Transition Matrix

A.D.K AL-Hiyali

Department of Agricultural Economics, Agriculture College, University of Anbar, Ramadi, 31001, Iraq

Hayder Hameed Blaw

Agiculture College, Al-Muthanna University, Samawah, Al Muthanna Governorate, 66001, Iraq

Najlaa Salah Madlul

Agiculture College, University of Tikrit, Tikrit, Saladin Governorate, 34001, Iraq


Received: 18 December 2023; Received in revised form: 19 March 2024; Accepted: 20 March 2024; Published: 28 March 2024

Copyright © 2024 A.D.K AL-Hiyali, Hayder Hameed Blaw, Najlaa Salah Madlul. Published by Nan Yang Academy of Sciences Pte. Ltd.

Creative Commons LicenseThis is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.


This research aims to predict the values of agricultural GDP in Iraq for the period 2019–2030 using the Markov Transition Matrix, whereby the fifth state was chosen due to the convergence of the predicted values with the most recent real values in the original time series. In addition, the predictive error or predictive accuracy of the selected state was better compared to other states. The reason for choosing this methodology in forecasting is that it is based on probabilities derived from old historical data, but this methodology does not need old historical data for the purpose of extracting predictive values, even if the time series includes long-time series data. The predicted values follow the same path as the original time series and are not affected by the general trend of the original data, which gives us an indication that there is a problem of stagnation of agricultural GDP. Therefore, a recommendation that can be given to economic policymakers, in particular the agricultural ones, is the existent need to address the problems that the agricultural sector has always suffered from in order to break this stagnation.

Keywords: Markov chains, Finite state machines, Predictive accuracy


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