Iranian economy as a state–petroleum economy with inflation and budget deficit challenges, has experienced both large and small business cycles in the last three decades. Obviously, studying the significant causes of these cycles can help the policy makers to design more accurate and efficient plans and policies. In this paper, we use a structural VECTOR AUTOREGRESSIVE (SVAR) MODEL to identify the effective causes of business cycles in Iran. The results show that between 1990 and 2012, budget deficit growth and real exchange rate growth are the most important causes of business cycles in Iran.

Investigating the effects of socioeconomic macroscale factors on road traffic fatalities is a useful step for adopting accurate economic decisions and reducing damages of these accidents in countries. But due to the distinct economic level and position of countries, these effects are different. This paper has paid to investigate the simultaneous relationship between gasoline price and GDP with traffic fatalities. The data used in this paper are the panel data of 91 countries spanning 2000-2014. In analyzing these data that applied separately for OECD and non-OECD countries, have used one of the proper MODELs in econometrics titled "VECTOR AUTOREGRESSIVE MODEL (VAR)". The results revealed in both groups of countries and with quite different trends, increase in gasoline price and GDP per capita significantly lead to decrease in traffic fatalities rate. This decrease is more especially about gasoline price in OECD countries. The reason for these results can be the removal unnecessary trips and increase in public transportation utility in effect of gasoline price increase and improve traffic safety conditions in effect of an increase in GDP.

Most of the economic plans of industrialized countries due to their dependence on oil imports, as well as oil exporting countries due to the importance of oil revenues in their economy, are formed based on oil price changes and developments. Oil price developments, which are interpreted as shocks, are mostly random in nature, and for this reason, the future shocks or the probability of their occurrence or both are uncertain and unknown for the actors of the oil markets. raw will be uncertain. In this research, the effects of international crude oil price shocks on economic fluctuations in Iran have been investigated using the structural VECTOR autoregression (SVAR) MODEL. In the short term, oil supply and specific demand shocks in the oil market led to an increase in oil prices and, as a result, curb economic production. An increase in oil prices caused by aggregate demand shocks will increase economic output. Aggregate economic demand shock acts as the main source of oil price shocks, and the specific demand of the oil market creates more restraining effects on economic production than aggregate supply and demand. Also, energy industries are less affected by supply and demand shocks than other industries due to the effect of government subsidies and the share system of state companies. The effect of specific demand shocks on oil price on industrial production is greater than supply shocks and demand shocks.

In the time series analysis, we may encounter situations where some elements of the MODEL are imprecise quantities. One of the most common situations is the inaccuracy of the underlying observations, usually due to measurement or human errors. In this paper, a new fuzzy AUTOREGRESSIVE time series MODEL based on the support VECTOR machine approach is proposed. For this purpose, the kernel function has been used for the stability and flexibility of the MODEL, and the constraints included in the MODEL have been used to control the points. In order to examine the performance and effectiveness of the proposed fuzzy AUTOREGRESSIVE time series MODEL, some goodness of fit criteria are used. The results were based on one example of simulated fuzzy time series data and two real examples, which showed that the proposed method performed better than other existing methods.

The multivariate time series MODELs as the linear stochastic MODELs were used to forecast monthly average of the Persian Gulf surface temperature (PGST). The time series data of temperature for the period 1854-2007 for six sea grid points were considered as the input file of multivariate time series. VECTOR AUTOREGRESSIVE processes were employed to perform multivariate time series. The auto-correlation function plot of the residuals for the fitted MODEL, based on the Akaike's information criterion, has indicated that the residuals were uncorrelated. The monthly values of the PGST from January 2008 to December 2009 as the test data have been forecasted using the fitted MODELs. The results have indicated that the correlation coefficient between the observed and predicted values for each grid point was about 0.99. The root mean square prediction error for each grid point was less than 0.7 degrees Celsius.

The present study analyzes the relationship between human capital shock, economic growth and housing prices. The statistical population of this research includes the provinces of Iran and a period of 2012 to 2017 has been chosen as the research sample, retrieved from existing economic data. Panel VECTOR AUTOREGRESSIVE MODELs were employed in order to analyze data. Obtained results from regression MODELs depict that, boosting/promoting human capital will increase the demand for housing and prices increasing. The results of impulse response function (IRF) depicted that response of human capital (housing price) to fluctuations in housing prices (human capital) and the response of production to fluctuations in human capital is higher than other variables responses. The results of variance analysis illustrate that the housing prices can be explained better by gross domestic product (18%) and human capital (1%), better than other variables. Hence, the growth of the housing market can have a positive influence on the economy. Also, the relationship between housing market prosperity and production is stronger than their relationship with the human capital variable.

This study provides some evidences in relationship between tools of corporate governance on information asymmetry around quarterly earnings announcement. Therefore selected a sample includes 326 earnings announcement of 158 listed companies in Tehran stock exchange during the years 1383 to 1388. To test the hypotheses has been used MODEL AUTOREGRESSIVE VECTOR. The variables have been used for corporate governance were percentage of institutional shareholders and percentage of independent directors and bid ask spread has been used as an index of information asymmetry. The results indicate that percentage of institutional shareholders and percentage of independent directors are not effective on bid ask spread around quarterly earnings announcement and indicate selector tools of corporate governance are not effective on information asymmetry around quarterly earnings announcement.