Prediction of Agricultural Macroeconomic Indicators Using Mixed Data Regression Models

Subject Areas : Agricultural Economics Research

zeynab omidvar ¹ , Ahmadali Ghaseminejad Raeeni ² , R. مقدسی ³ , amir mohamadinejad ⁴

1 - Department of Agricultural Economics, Extension and Education, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Economics, Sirjan Branch, Islamic Azad University, Sirjan, Iran
3 - Department of Agricultural Economics, Extension and Education, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 - Department of Agricultural Economics, Extension and Education, Science and Research Branch, Islamic Azad University, Tehran, Iran

Received: 2020-11-01 Accepted : 2023-12-01 Published : 2024-01-21

Keywords: Macroeconomic indicators, midas regression, long-term and short-term forecast pattern,

Abstract :

Introduction: Agriculture plays a key role in meeting food security goals of the country. Thus, knowledge on the future trends of agricultural indicators seems to be of vital importance for policy-making. On the other hand, access to data in agriculture is limited, while most of the available data are in different frequencies. This study is an empirical application of mixed data regression models: which considers this issue.
Materials and Methods: In order to touch study objectives, the MIDAS model is specified and estimated using time series (combination of quarterly and monthly information) data for the period from 2013 to the end of 2018. The variables include agricultural exports, agricultural imports, inflation and exchange rate in monthly frequency, and agricultural value added, temperature and precipitation in quarterly frequency.
Findings: The results confirmed good prediction power of the model. It is concluded that the exchange rate and inflation have a significant impact on all equations, and the temperature has a significant effect on value added and exports. Moreover, precipitation did not show a significant effect.
Conclusion: This study showed the capability of MIDAS approach in modeling of variables with different frequencies.

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