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The purpose of this study is to investigate the economic effects of world food and oil price shocks on macroeconomic variables (industrial output growth, inflation, stock price indices, lending rate, and real exchange rate) in Iran. For this purpose, the Structural Vector Auto Regressive (SVAR) method is used to examine the autonomous and simultaneous effects of food and oil prices within 3 models. This study uses monthly data from 21th March 2001 to 20th March 2011. The results show that oil price shock has small effect on industrial output growth. The biggest effect of the oil and food price shocks is observed on the exchange rate. About 5 percent of the variation in oil and food prices is explained by inflation shock. Furthermore, results from the simultaneous study of shocks to the oil price and global food price indicate that oil shocks significantly affect global food prices.  

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In recent years, consumers’ concerns about environmental and health issues related to food products have risen; consequently, the demand for organically grown products has increased. In this respect, the aim of this study was to investigate factors affecting consumers’ potential willingness to pay premium prices for organic food products in Tabriz, Iran. An Ordered Logit regression model was applied to obtain the value of willingness to pay and determine the factors affecting it. Survey results showed that about 95 percent of the respondents were willing to pay a premium; while about 10 percent of them were willing to pay more than 35 percent premium for organic food products. Results revealed that factors like "individual’s income", "family dimension", “environmental concerns" and "wholesome diet", besides "the general criteria of shopping", and "consumers’ awareness of these products’ characteristics" significantly increased consumers’ willingness to pay a premium. According to the results, married respondents as well as females were willing to pay a higher premium. In addition, those who had children younger than 10 years old, elderly, or people with family members having special disease were significantly willing to pay a higher premium price for these products. More than 80 percent of the consumers mentioned "absence of certifications and organic labels", "lack of advertisement", and "higher prices" as their most important problems in purchasing organic food products.

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Fisher-Seater's approach is applied to the Iran's economic data 2008 to test the long-Run neutrality and super neutrality of money during 1988-2008. Our results support the neutrality of M2 w real GDP and real agricultural output. For nominal agricultural output neutrality of M2 is strongly rejected. The result of neutrality for the nominal GDP varies depending on the unit root test. The results also showed that the super neutrality of M2 with respect to real GDP is confirmed.

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Given the large weight of food in the households’ consumption basket and its limited substitutability with other goods, food price fluctuations are of sizeable impacts on overall consumer prices. The reaction of the food prices to monetary shocks has been the subject of much empirical researches in the recent years. This study examines the impact of monetary shocks on food prices in Iran. To do this, the study adopts Johansen-Juselius and Error-Correction models using time-series data over the period 1973-2008. Using Hodrick-Prescott filter, the monetary shocks were obtained. The results showed that in the long-run positive monetary shocks have significant effects on food prices in Iran. Therefore, policies and strategies should be such that minimize the negative effects of monetary shocks on the food prices. 

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One of the main challenges facing the Agriculture Bank of Iran regarding lending loans is the high probability of default by farmers. Several factors could be involved in this issue and should be considered in order to control and reduce the risk in the failure of repayment. This study aimed to examine the factors affecting the repayment performance of agricultural loans in the city of Maragheh in East Azarbaijan Province of Iran. Required data were obtained from a sample of 779 individual farmers who had previously received loans from Agricultural Bank during the period 2004-2008. Nested Logit Model (NLM) was applied for analyzing the data and, for this purpose, STATA software was used in the study. Results revealed that having an activity besides farming, extension of the repayment period of the loan, and large volume of received loans are the factors that had significant negative impacts on loan repayment. On the other hand, factors including high interest rates of loans, having collateral of guarantor, services received from the banks, and long term maturity period for the loans increase the probability of timely loan repayment significantly. 

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In this study, the factors creating systematic risk for dry farming wheat crop in Iran were investigated. Using production functions as well as spatial econometric approach, the effects of changes in climatic parameters such as temperature and precipitation, and also input levels of seed, urea, and phosphate fertilizers in warm, moderate, and cold climates were examined. The results showed that the fluctuations of climatic parameters in the three climates were severe enough to be identified as systematic risk factors. The findings also indicated that, in a warm climate, lack of sufficient heat during cultivation time (October), overheating during initial growth months (December and January), lack of sufficient precipitation during initial growth months (November and December) and inadequate seed and urea fertilizer and overusing phosphate fertilizer were the systematic risk factors. In moderate climate, these factors included lack of sufficient heat in cultivation time (October) and in late harvest time (July), lack of sufficient precipitation in the cultivation time (October) and lack of urea fertilizer and seed phosphate overuse. Finally, in the cold climate, insufficient heat in vegetative growth time (March), inadequate precipitation in the cultivation and initial growth time (October and December), and also lack of phosphate fertilizer and seed overuse were identified as the systematic risk factors.

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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.