Journal of Economic Modeling Research

Money laundering is an illegal practice that legitimizes the income from illegal activities during a legitimate process.Trade-based money laundering (TBML) as one of the newest and most complicated types of money laundering has negative effects on economic, social and political aspect of a society.The most important objective of the present study is to investigate the effect of various factors on trade based money laundering in Iran using the Ferwerda Gravity model.For this purpose the effective factors on trade base money laundering between iran and some selected trade partners  are investigated by the use of a random effect model during the period 1999-2012. The results indicate that a great significant part of the trade based money laundering flow between Iran and selected trade partners can be explained by the the Ferwerda Gravity model. Accordingly, gorss doimestic product(GDP), trade volume, geographical, cultural, population and attractiveness variables have a significant effect on the amount of trade based money laundering in Iran.This means that with the increase in trade flow, money laundering opportunities resulted from the trade channel, that is hidden in it, will also increase. These results can be used by policy makers for designing policies to combat money laundering particularly coming from trade channel.

One of the factors that have a significant impact on the economic development of countries is reliance on foreign trade, and due to the dependence of countries on export earnings and the import of foreign trade, it plays an essential and indisputable role in the growth and development of different sectors. Foreign trade in agricultural products has an important role in expanding the export and import of various countries due to its high and stable value. Among the agricultural products, saffron is one of the most important export commodities of agriculture, which contributes greatly to the creation of agricultural value added. This study examines and analyzes Saffron's foreign trade network among 11 active countries including Iran in this area. The required data and statistics were collected during the years 2007-2016. Considering the geographical dispersion of the studied countries, a spatial panel model was used to analyze the factors affecting the value of saffron trade. The results of estimating OLS methods, spatial interruption and spatial error showed that import price variables per gram, export price per gram, export volume, export standards index, gross domestic product, exchange rate and government support index in agricultural sector is significant and has a positive effect on the value of trade. Positive and significant spatial dependency coefficient shows that neighborhood is an important role in increasing or decreasing trade. This means that, as long as the value of saffron trade in neighboring countries increases, the value of trade in the target country will increase as the size of the coefficient.

The existing trade models suggest that for tradable goods potential partners can be many, but eventually only one (the one offering the best price) should be selected, therefore relatively few (unidirectional) trade links will appear between countries. If the structure of international trade flows describes as a network, trade link would give rise between countries. This paper exploit recently-developed indicators based on network analysis such as node-degree, node-strength and node-disparity, and second-degree characteristics such as node-clustering and centrality indicators to investigate the pattern of international trade pattern followed by Iran and its Asian partner. The results of this study show that East Asian countries, have had a greater increase in the number of trade partners. Iran and its trading partners in Asia, is growing trade links with countries that have more trading partners. Nearest neighbor degree index show that selected countries are looking to improve relations with countries that have more similarities with his own country. Based on the centrality, it was found that only China with the centrality index of .97 is in the core of global trade network. Emirates, Taiwan, Korea and Thailand respectively with values of .94, .92, .94 and .91 are in the inner-periphery and Turkey with a value of 0.87 is in secondary-periphery. Iran with a value of 0.72 is in outside of the global trade network

Exchange rate changes could impact on prices. Whether exchange rate pass through to prices is complete or incomplete is an interesting question in analyzing impacts of exchange rate policy. An important aspect of exchange rate pass through is in producer price index and in its sub-indices. Our aim is to analyze the effects of exchange rate changes on producer sub-indices. To do that we have used of the structural vector autoregressive (SVAR) methodology. Our data contains 1991 Q2-2017 Q1. The results show that there is a significant heterogeneity in exchange rate pass-through to producer prices. Our results showed that there is complete exchange rate pass through in industrial prices but agricultural and service prices show incomplete pass through. According to previous studies, the difference could be related to different market structure in industrial activities compared to agriculture and service activities.

International balance of payments is one of the most common criteria for measuring the flow of trade and capital transfers in an open economy. The three main components of this balance are: trade balance, current account (or difference between export and import of goods and services) and capital account. In this study, factor augmented vector autoregressive model (FAVAR) was used to evaluate the effects of balance of payments shocks on macroeconomic variables in the Iran economy in periode 1989-2017. The factors used in this study included economic growth, oil revenues, money growth, inflation, exchange rates and interest rates. The results show that the shock from the current account and capital account led to an increase in production, consumption and investment. The reaction of nominal sector variables such as inflation and interest rate to positive shock was also positive. Comparison of the results of this study shows that incorporation of hidden variables and factors into the model resulted in faster response of macroeconomic variables to the shocks entered by the balance of payments components.

The effects of International financial integration on the fluctuations of variables in response to shocks are a matter of heavily concentrated literature of the business cycle in recent years. In this paper, a New Keynesian DSGE model is developed in which there is a channel for capital account changes through the foreign deposit's inflow and outflow. Then the effects of financial integration are simulated. The integration factor is defined by the percentage of the total foreign deposits absorbed by the banking system. This coefficient could change due to changes in effective domestic interest rate and global interest rate. This paper shows in presence of oil shocks, the fluctuation of production, consumption, real exchange rate and variables of the banking system such as deposits and loans, is higher in financial integration but there is no significant difference in inflation. In presence of technology shocks, there is no significant difference.

The corona virus has turned a health crisis into an economic crisis and its spread has led to strong negative reactions from stock markets in various countries and price fluctuations in many macroeconomic variables. On the other hand, the spread of the virus provides a basis for examining the effects of its prevalence on stock markets, economic variables and the power of influence and the speed of information dissemination in times of crisis in these markets. The aim of the present study was to investigate the effect of corona virus on the stock markets of 75 countries and the variables of oil, gold, silver and copper by comparing complex networks before and after the outbreak of the virus. Also, for the calculation section, matlab statistical software has been used and for drawing the networks, the maximum filtered flat graph method has been used with the help of daily data in the period from June 2019 to March 2020. the results show that before the outbreak of coronavirus, stock markets tended to move in small continental groups, but the outbreak of the virus led to negative group movements with high correlation for these markets, positive or negative information spreads 32% faster than before on the stock market network, also stock markets are twice as influential as they were before the outbreak. The corona virus has directly led to a 40% drop in stock markets. on the other hand, the virus has caused fluctuations in the global variables of oil, gold, silver and copper, which each respectively affected 55%, 32%, 28% and 35% of stock markets, the impact of these variables before the outbreak of the virus was 31%, 20%, 16% and 18% of stock markets, respectively.‌ it is important to note that in crises due to the collective movements of stock markets, price stability in central stock markets and macroeconomic variables are very important to control and reduce the negative effects of the crisis on stock markets.