This study examined the effects of economic globalization on economic growth, using Vector Auto Regression (VAR) in order to separate the effects of short- and long-term of this phenomenon. To investigate the relationship, the dynamic interactions of shocks created in the model have been analyzed by using Forecast Error Variance Decomposition (FEVD) and Impulse Response Functions (IRF). In this path, the economic and financial openness index definition, annual exports, imports, entry and exit of capital and GDP over the period 1390-1357 have been used. The results of this study show that the index of trade and financial liberalization has a significantly positive effect on economic growth. The Convergence tests show that there is only one Vector convergence between variables of the model.

Business cycles are the characteristics of the real estate market that get less attention in economics literature. Real estate market on the one hand is relatively high affected by economic business cycle and on the other hand significantly can affect economic as it can cause big financial crisis. Such importance of house market makes analyzing of real estate market Inevitable. In this paper close attention is paid to business cycle in real estate market in Iran and private residential investment behavior using Markov-switching VAR model is analyzed. Results achieved from Iran economy shows probability of staying in boom period is more than transition from it (95% vs. 5%) and probability of transition of stagnation period is more than staying in that (73% vs. 27%). According to these results expected duration of boom period is five time longer than expected duration of stagnation period.

Income distribution is of crucial importance for policymakers from the social justice viewpoint. In recent years, financial tools and intermediaries have been developed in the global economy. Thus, investigating the impact of financial development on the income inequality has attracted the attention of economic researchers. Financial development affects income distribution through both channels of economic growth (directly) and increasing access to financial services (indirectly). Many studies have been conducted regarding the effects of financial development on income distribution in Iran with different methods and indicators, and even contradictory results. This study analyzes the effect of financial development on income inequality using various indicators and the structural Vector Auto-Regression (SVAR) approach. The findings confirm the direct effect of financial development on income inequality, or the inequality-narrowing hypothesis, though they have no decisive implication on the indirect effects of financial development on income inequality, or the inequality-broadening hypothesis.

This paper has two aims. The first is forecasting ination in Iran using Macroeconomic variables data in Iran (Ination rate, liquidity, GDP, prices of imported goods and exchange rates), and the second is comparing the performance of forecasting Vector Auto Regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's ination is forecasted. The comparison of performance of forecasting models used to forecast Iran's ination has been done based on the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the models. Due to the annual values of Ination, liquidity, GDP, prices of imported goods and exchange rates at free market to estimate di erent models in this paper and compare root mean square error and Mean Absolute Percentage Error of models by which ination has been forecasted, neural network model had better performance than others models in forecasting Iran' s ination. Indeed root mean square error and Mean Absolute Percentage Error of neural network model have less value rather than root mean square error and Mean Absolute Percentage Error of other forecasting models.

One of Iran’s major lucrative export products, offered by a limited number of countries in the world, yet having a large range of customers is pistachios. The study taking advantage of the statistics from 1980 to 2010 and with Vector Auto Regression approach (VAR), Impulse Response Analysis and forcast error variance decomposition, factors affecting Iran’s export supply and demand were analyzed. The results of co-integration model reveals the fact that each of the variables of average income of importing countries, the real exchange rate and domestic production of pistachios has a significant as well as positive relation in long-term while production in other countries in addition to the domestic price of the product has a significant and negative relation with supply and demand of the exporting product in long-term. The analysis of forecast error variance decomposition, additionally, manifests the most effect on the fluctuations of supply of pistachios export in Iran is due to the variable itself, while the most effective factor on fluctuations of the global demand of the export, is the variable of Iran’s exporting supply factor, after which the same is true for the exporting price in long-term.

Value at risk (VaR) risk assessment and diagnosis method that uses standard statistical techniques that are routinely used in other technical fields, is used. The contract, the value at risk in a given period the maximum expected loss at a given confidence level is measured. This article is designed to measure a sequence of high-risk deals with the calculation of Liquidity-adjusted Intraday (LIVaR). Hence, our goal is clear review aspects related to the size of the company's internal liquidity. With the reconstruction of classified information, and significant changes in real output and efficiency without friction (planned) occurred and these two variables were modeled jointly. Risk related to planned cash expenses, was determined at a later stage.

Oil as the main source of energy and engine of economic growth, is an essential input in the production process, that its consumption has increased significantly along with the modernization of the economy. The subject of the present study is the asymmetric analysis of oil price in Iran. In this regard, the effect of oil price on economic growth in two models, considering oil revenue and without it, has been estimated using the nonlinear AutoRegression distributed lag method (NARDL). The results of the study for the period 2018-2000 indicate that the effect of oil price on economic growth, containing oil revenue was asymmetric but without oil revenue was symmetric.

Fiscal multiplier is one of the most important factors which governments and economic policymakers rely on to conduct fiscal policy. This multiplier, which shows the effectiveness of fiscal policy in stimulating domestic production and economic stabilization, has always been a contentious issue among economists and researchers, because there is no theoretical agreement on the size of this coefficient. Therefore, several researchers have tried to empirically estimate the Fiscal multiplier. This effort, which has doubled after the global crisis of 2007-2008 due to the significant role of this policy tool in reducing crises, is the main purpose of this study. This paper estimates the instantaneous and cumulative Fiscal multiplier using quarterly data of Iran economy during 1990 to 2017, using a non-linear threshold Auto-regressive model. The results of TAR test and TVECM test showed that the integration of some variables used in the research is nonlinear. Similarly, the Co-integration relationship between the variables is nonlinear. Also estimation of TVAR model and simulated nonlinear impact response functions showed that the multiplier of government expenditures during the recession is greater than the boom period. This is opposite for tax. Also the largest multiplier among the three fiscal policy instruments related to current government expenditures.