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Few studies have recently supported the preposition that housing price changes in one location can be affected not only by its history but also by housing price changes in other locations (Ripple effect). There are many reasons why housing price changes in some areas may lead housing price movements in the other areas. The possible causes include structural differences and economic interdependence between areas, migration as well as informational factors. This paper examines spatial effects of house price dynamics within Tehran. The first hypothese is that house prices have started rising first in the north and then spread out over the rest of the city. The second hypothesis is that there is a strong price relationship between contiguous districts. Spatial autoregressive (SAR) and vector error-correction (VEC) models are emplyed to estimate the empirical models using quarterly panel data of Tehran housing markets over the period 1991 to 2006. The results confirms that there is a positive spatial interrelationship among districts. Moreover, the findings indicate the evidence of leading role of the district 1 in the northern half of the city while district 2 seems to have a more prominent effect on the center and the west. Furtheremore, the price interdependence of contiguous districts in the north is seen to be more significant and stronger than the south.

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The aim of this paper is twofold. It first investigates the demand for international reserves using panel data for 32 developing countries during the period 1975 – 2004. To this end, an Error Correction Model (ECM) approach is employed to estimate the empirical model and then a capital asset pricing model (CAMP) is used to determine the composition of the foreign exchange reserves. A dummy variable is included in the model to consider the effect of changing exchange rate system on the demand and the composition of international reserves. The results show that there is a negative relationship between exchange rate flexibility and the demand for international reserves.

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Rapid increase in housing price has been a national difficulty for the people and public officials in recent years in Iran. Therefore, the study of factors affecting increased housing prices is of great importance. In this paper, the effects of some key variables, including land price, construction cost, the volume of bank loans to housing sector, exchange rate, stock price index, the number of residential buildings and household income on the price of housing have been studied in Kermanshah. For analyzing the effects of above-mentioned variables on price of houses, an auto regressive distributed lag model has been estimated using quarterly data over the period 1991-2009. Additionally, the error correction model was estimated in order to evaluate the adjustment speed of dynamic model to the long-run one. The estimation results indicate that the macroeconomic variables are of high power in explaining the behavior of house price and its fluctuations.

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Since various economic sectors, in particular housing sector, need to bank loans, the variations in lending behavior of banks due to changes in key economic variables may jeopardize the sound economic activities. In this study the lending behavior of Bank Maskan of Iran was modeled by a Vector Auto-regression (VAR) model during 1991-2011. The results of long run Vector Error Correction Model (VECM) indicated that the broad money supply, inflation rate and stock price fluctuations have indirect effects on lending behavior of Bank Maskan, however the effect of exchange rate variations is positive. In addition, the results of short run VECM showed that variations in the broad money supply have direct effects on lending behavior of Bank Maskan, but inflation rate, exchange rate and stock price fluctuations have no significant effects.

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Trade balance is regarded as both main macroeconomic factor and strategic constraint in developing countries. Exchange rate, which is defined as parity relationship between national currency and foreign currencies, is a vital determinant of countries’ trade balance. As the real effective exchange rate measures the changes in prices and relative costs by a common currency, it is the most popular indicator to measure competitiveness. On one hand, fluctuation of this index represents disequilibrium in the economy, and on the other hand, it is the cause of more instability. Since the direction and size of the effects of real exchange rate on trade balance is an important macroeconomic issue, this articleinvestigates the real effective exchange rate changes on trade balance in Iran and its’ major partners using the Vector Error Correction Model ( VECM ) over the period 1993-2011. The results indicate that the real effective exchange rate volatility reduces trade balance only for Germany in the short run and rises it for Italy in the long run.

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National accounts data are of the most important statistical tools in planning and making economic policy. Therefore, forecasting the main economic variables in the economy is of great importance. Economic growth is one of the key macroeconomic variables, which gets top priority in forecasting.  The purpose of this study is to identify the appropriate methodology for forecasting economic growth in Iran. This study introduces fuzzy regression model and its’ ability to forecast economic growth of Iran in comparison with Error Correction Model (ECM). To do this, the Iran’s GDP is modeled through ECM and Fuzzy regression models using annual data form 1959 to 2001. Then, Iran’s GDP growth is predicted for 2002-2012. Finally, the performances of these models are compared using common criteria for evaluating forecast accuracy including mean absolute error (MAE), root mean square Error (RMSE), mean absolute percentage error (MAPE) and Theil’s inequality coefficient (TIC). The results indicate that the performance of fuzzy regression is far better than that of ECM in predicting GDP growth in Iran. Moreover, forecast accuracy of fuzzy regression model is of statistically significant difference in comparison with ECM model.

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Among the food products, grains play an important role in the consumption patterns of people, especially in the developing countries. Since Iran's main source of public dietary energy comes directly from grains, investigating and identifying the determinants of import of these products can be an important step towards food security. Most experimental studies consider import of grains as only a function of relative prices and real income, whereas, income inequality is also a variable affecting the import of grains. The present study evaluates the effect of income inequality on the import of grains in Iran's economy during the years 1969-2009. For this purpose, the relationship of grain import with gross domestic production (GDP), grain production, real exchange rate, and income inequality was evaluated for Iran by using the Vector Error Correction Model (VECM). The results indicate that the relationship between income inequality and grain import is positive and its coefficient is +0.55%. This implies that 1% increase in income inequality increases grain import by 0.55%. Also, the effect of gross domestic production on grains import is positive and the real exchange rate and grains production variables have a negative and significant effect on grains import. 

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Volatility and imperfect price transmission in food markets always impress the welfare of producers and consumers, especially in the developing countries. Therefore, the purpose of this study was to investigate the price relationship in vertical market levels (i.e. farm gate, wholesale and retail) of rice as a staple food for Iranians, using the Vector Error Correction Model (VECM) and the Generalized AutoRegressive Conditional Heteroskedastic (GARCH). The data used was based on monthly observations of prices in Kamfiroz Rice Market from April 1997 to March 2015. Results showed that the direction of Granger causality and partial price transmission were from farm gate to retail market as well as from wholesale to farm gate level and retail market to wholesale, such that, if wholesale prices increase by 1%, farm gate prices will increase about 0.37%. Also, if retail prices increase by 1%, then wholesale prices will increase by about 0.36%. In addition, if farm gate prices increase by 1%, then retail prices will decrease by about 0.08%. Results also implied that retail and wholesale price volatilities have positive spillover effects on the volatility of farm gate prices (i.e. 0.50 and 0.31, respectively). In addition, retail prices are more sensitive to wholesale prices and more volatile (i.e. 0.56) than the others. Finally, in order to increase the transparency of information and increase the efficiency of price transmission in Kamfiroz Rice Market, it was suggested that marketing cooperatives of this product be increased and supported more.