پيش بيني رشد بخش كشاورزي ايران: رهيافت داده هاي تركيبي با تواتر متفاوت (MIDAS)

Forecasting Iran's Agricultural Sector Growth: Using Mixed-frequency Data Sampling (MIDAS) Model

اﻣﺮوزه در ﻣﻮﺿﻮﻋﺎت اﻗﺘﺼﺎدي و ﺑﺎزرﮔﺎﻧﯽ، ﭘﯿﺶﺑﯿﻨﯽ ﺑﻪﻋﻨﻮان ﯾﮑﯽ از ﻣﻬﻢﺗﺮﯾﻦ ﺷﺎﺧﺼﻪﻫﺎي ﻋﻠﻤﯽ، روز ﺑﻪ روز ﭘﯿﺸﺮﻓﺖ ﻣﯽﮐﻨﺪ و ﭘﯿﺶﺑﯿﻨﯽ ﻣﺘﻐﯿﺮﻫﺎي ﮐﻼن اﻗﺘﺼﺎدي ﺑﺮاي ﺑﺮﻧﺎﻣﻪرﯾﺰان و ﺳﯿﺎﺳﺖﮔﺬاران و واﺣﺪﻫﺎي اﻗﺘﺼﺎدي، از اﻫﻤﯿﺖ وﯾﮋهاي ﺑﺮﺧﻮردار اﺳﺖ. ﺑﺨﺶ ﮐﺸﺎورزي، ﺑﻪﻋﻨﻮان ﺑﺨﺶ ﺗﻮﻟﯿﺪﮐﻨﻨﺪه ﻣﺤﺼﻮﻻت راﻫﺒﺮدي و ﺗﺄﻣﯿﻦ ﮐﻨﻨﺪه ﻣﻮاد ﻏﺬاﯾﯽ ﻣﻮرد ﻧﯿﺎز ﺟﻤﻌﯿﺖ رو ﺑﻪ رﺷﺪ ﺟﺎﻣﻌﻪ، ﺗﺄﺛﯿﺮ زﯾﺎدي در ﺑﺴﯿﺎري از ﺗﺼﻤﯿﻢﮔﯿﺮيﻫﺎي اﻗﺘﺼﺎدي، اﺟﺘﻤﺎﻋﯽ و ﺳﯿﺎﺳﯽ دارد. ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ اﻫﻤﯿﺖ ﺑﺨﺶ ﮐﺸﺎورزي در اﻗﺘﺼﺎد ﮐﺸﻮر و ﻧﯿﺰ وﺟﻮد ﻋﻮاﻣﻞ ﺗﺄﺛﯿﺮﮔﺬار ﻣﺘﻔﺎوت و ﻏﯿﺮﻗﺎﺑﻞ ﮐﻨﺘﺮل، ﺳﻌﯽ ﻣﯽﺷﻮد از روشﻫﺎﯾﯽ در ﭘﯿﺶﺑﯿﻨﯽ اﺳﺘﻔﺎده ﺷﻮد ﮐﻪ ﺑﻪواﺳﻄﻪ آﻧﻬﺎ، ﺗﺨﻤﯿﻦ ﺑﻪ واﻗﻌﯿﺖ ﻧﺰدﯾﮏ و ﺧﻄﺎ ﺑﺴﯿﺎر ﮐﻢ ﺑﺎﺷﺪ ﺗﺎ رﺷﺪ اﻗﺘﺼﺎدي اﯾﻦ ﺑﺨﺶ را ﺑﻪدرﺳﺘﯽ ﭘﯿﺶﺑﯿﻨﯽ ﮐﺮده و ﺳﯿﺎﺳﺖﻫﺎ و ﺑﺮﻧﺎﻣﻪﻫﺎي ﻻزم ﺑﺮاي ﺑﻬﺒﻮد ﺟﺎﯾﮕﺎه اﯾﻦ ﺑﺨﺶ، ﻃﺮحرﯾﺰي ﺷﻮد. در اﯾﻦ ﻣﻘﺎﻟﻪ، از اﻟﮕﻮي دادهﻫﺎي ﺗﺮﮐﯿﺒﯽ ﺑﺎ ﺗﻮاﺗﺮ ﻣﺘﻔﺎوت )MIDAS ( ﮐﻪ اﺧﯿﺮاً ﮔﺴﺘﺮش زﯾﺎدي داﺷﺘﻪ، ﺑﻪ ﭘﯿﺶﺑﯿﻨﯽ رﺷﺪ ارزشاﻓﺰوده ﺑﺨﺶ ﮐﺸﺎورزي ﭘﺮداﺧﺘﻪ ﺷﺪه اﺳﺖ. ﻣﻘﺎﯾﺴﻪ ﭘﯿﺶﺑﯿﻨﯽﻫﺎي اراﺋﻪ ﺷﺪه ﺗﻮﺳﻂ اﻟﮕﻮي ﺑﺮآورد ﺷﺪه ﺑﺮاي رﺷﺪ ارزشاﻓﺰوده ﺑﺨﺶ ﮐﺸﺎورزي ﺑﺎ دادهﻫﺎي واﻗﻌﯽ، ﺣﺎﮐﯽ از ﻗﺪرت ﭘﯿﺶﺑﯿﻨﯽ دﻗﯿﻖ اﻟﮕﻮ اﺳﺖ. اﯾﻦ اﻟﮕﻮ، ﻧﺮخ رﺷﺪ ارزش اﻓﺰوده ﺑﺨﺶ ﮐﺸﺎورزي را ﺑﺮاي ﺳﺎﻟﻬﺎي 1396-1400 ﺑﻪ ﺗﺮﺗﯿﺐ، 5/29 ،2/92 ،2/53 ،3/215 و 5/99 درﺻﺪ ﭘﯿﺶﺑﯿﻨﯽ ﮐﺮده اﺳﺖ

Today, forecasting of economic and commercial variables as an important scientific field is developing, and forecasting of macroeconomic variables is of special importance for planners, policy makers and economic enterprises. The agricultural sector, as a producer of strategic products and provider of food for the growing population, has a great influence on economic, social and political decisions. Considering the importance of the agricultural sector in Iran as well as the existence of different and uncontrollable influential factors, the researchers who focus on agricultural sector’ growth, try to use methods of forecasting in order to get results close to reality, reduce the prediction errors, and design policies and plans to improve the place of this sector. In this paper, the mixed frequency data-sampling model (MIDAS) has been used to predict the growth of agricultural sector’ value added. Comparison of the model predictions with actual data indicates the predictive power of the model. This model has predicted the growth rate of agricultural sector's value added over the period 2017-2021 by 3.215%, 2.53%, 2.92%, 5.29%, and 5.99%, respectively.