درخت حوزه‌های تخصصی

This study aims to investigate the relationship between liquidity and asset price fluctuations in the Iranian financial market applying innovative methods of averaging. The findings are useful for policymakers because the surveys can be used in monetary policy decisions. The data used in this research are quarterly from March 2006 to April 2020. To this end, the statistics from the economic time-series database of the Central Bank of the Islamic Republic of Iran and other related sources have been used. Bayesian Model Averaging (BMA) has successfully been used in the experimental growth literature as a way to overcome the sensitivity of the results to the characteristics of different models. The variables are the real liquidity as a dependent one and fluctuations in total stock price, total housing price, central bank assets, the government debt to banks, exchange rate, and the coin price are considered independent ones. In terms of the movable assets, the results show a positive and significant relationship between fluctuations in real total housing prices and liquidity and a negative relationship between Bahar-e Azadi Iranian Gold Coin fluctuation and liquidity. For immovable assets, the real net fluctuation of central bank assets and the government debt to banks have a positive relationship with liquidity. There is a negative relationship of real total stock price fluctuation and real exchange rate fluctuation with the liquidity.

The lack of a comprehensive information system and application model in the supply chain of agricultural products in Iran has caused this part of the country's economy to be ineffective despite its potential.Therefore, the aim of the current research was to investigate the Barriers against the orchards production chains  in Meshginshahr located at Ardabil province, Iran. To reach the aim of the study,  the semi-structured interviews were used to collect research data from 16 interviews including farmers, faculties and managers in the field of farming production chains. Then we analyzed the data by applying the Strauss and Corbin method and the paradigm model through Max QDA software. Sampling was theoretical sampling and was done using targeted and snowball methods. Based on that, 16 interviews were conducted with gardeners, university professors and managers in the field of Barriers to the development of agricultural production chains. The open codes included 38 concepts and the core codes also included 44 major categories, which were finally identified into four groups of selective categories including Barriers to i) product production; ii) input supply; iii) product distribution; and iv) customers . Based on the findings of the research on the risk of supply of input resources, the weakness of regulations and rules in supply of inputs, production information barriers, strategic production barriers, competition barriers in production, environmental risks, planning-management, financial-credit, technical-technical barriers, Product distribution cost barriers, lack of regulations in the distribution system, and sales barriers; Barriers related to the production sector are of the Barriers to the development of production chains of agricultural products. Barriers related to the production sector are one of the Barriers to the development of production chains of agricultural products.