نشریه اقتصاد پولی، مالی
پیاپی 10 (پاییز و زمستان 1394)

Financial markets development is one of the major factors in economic growth. According to the literature, financial section could affect economic growth in two ways: enhancing resource allocation and hastening technology development. This study pin out the first way, i.e., the resource allocation. To this end, this study tries to get an optimized credit allocation between oil-related and non-oil sections.
Iran’s agriculture part is one of the areas that can have an important effect on the growth of country’s economy. Concerning this, variables that can increase value added agriculture have been concentrated on and the government is supporting them. One of these policies is granting loanable facilities from specialist banks to the agriculture part, which was in the specialist banks agendum during recent years.
This study divides GDP to oil-GDP and non-oil GDP and uses GDP growth as a proxy to economic growth. After recognition of the importance of bank credit through optimizing credit allocation between oil and non-oil sections, it turns to clarify the issue in the subsection. Results show that bank credits are more efficient in non-oil section and also the agriculture subsection.
Theoretical Framework: Greenwood and Jovanovic (1990) developed a theoretical model to find that the impact of financial development on economic growth is dependent on the transitional cycles in the economy. Austrian-based credit cycle theories (Hayek, 1933, 1935; von Mises, 1912) and capital-based macroeconomics (Cochran, Call, & Glahe, 1999; Garrison, 2001) generally argue that financial development and credit expansion, especially through money creation, may cause overinvestment problems that lead to unsustainable economic growth. The economic growth, especially in small oil basted economies may experience larger fluctuations according to the credit boom explanation of the business cycle (White, 2006). Thus, the relation between financial development and economic growth in small natural resource-based economies is a non-trivial question and yet to be empirically investigated.
Several empirical studies, using macro and industry-level data, have concluded that the development of financial intermediation has a significantly positive effect on economic growth. King and Levine (1993) provided the most comprehensive empirical work where using cross-sectional data from 80 countries. They found a positive relationship between bank credit and economic growth. Efficient allocation of funds through financial institutions leads to economic growth. Other studies including Levine and Zervos (1998), Levine (1998), and Beck and Levine (2003) found similar results. Eschenbach (2004) reviewed the majority of empirical studies and concluded that the direction of causality between financial development and growth varies across countries, regions and even variables employed by these studies.
Bayesian model averaging (BMA) is an empirical tool to deal with model uncertainty in various milieus of applied science. In general, BMA is employed when there exists a variety of models which may all be statistically reasonable but the most likely result in different conclusions about the key questions of interest to the researcher. As Raftery (1995, p. 113) noted, in this situation, the standard approach of selecting a single model and basing inference on it underestimates uncertainty about quantities of interest because it ignores uncertainty about model form." Typically, though not always, BMA focuses on which regressors to include in the analysis. The allure of BMA is that one can quickly determine models, or more specifically, sets of explanatory variables, which possess high likelihoods. By averaging across a large set of models, one can determine those variables which are relevant to the data generating process for a given set of priors used in the analysis. Each model (a set of variables) receives a weight and the final estimates are constructed as a weighted average of the parameter estimates from each of the models. BMA includes all of the variables within the analysis, but shrinks the impact of certain variables towards zero through the model weights. These weights are the key feature for estimation via BMA and will depend upon a number of key features of the averaging exercise including the choice of prior specified. These difficulties made us to apply Bayesian Model Selection (BMS) to conquer BMA model problems. BMS uses the Markov Chain Monte Carlo (MCMC) samplers to gather results on the most important part of the posterior distribution.
The MCMC sampler randomly draws a candidate model and then moves to this model if its marginal likelihood is superior to the marginal likelihood of the current model. In this algorithm, the number of times each model is kept will converge to the distribution of posterior model probabilities. There are two different MCMC samplers to look at models within the model space. These two methods differ in the way they propose candidate models. The first method is called the birth-death sampler. In this case, one of the potential regressors is randomly chosen; if the chosen variable is already in the current model Mi, then the candidate model Mj will have the same set of covariates as Mi but drop the chosen variable. If the chosen covariate is not contained in Mi, then the candidate model will contain all the variables from Mi plus the chosen covariate; hence, the appearance (birth) or disappearance (death) of the chosen variable depends on if it already appears in the model. The second approach is called the reversible-jump sampler. This sampler draws a candidate model by the birth-death method with 50% probability and with 50% probability the candidate model randomly drops one covariate with respect to Mi and randomly adds one random variable from the potential covariates that were not included in model Mi.
After decomposing true prior to several economic sections, it is turned out that non-oil section of Iran’s economy has the potential to have more growth than oil related section and also from the non-oil sections, that is, the agriculture and industrial sub-sections , the agriculture sub-section was optimizing the credit resources more efficiently than the industrial one. This study also determines 5 models, accompanied by the highest posterior probability, that place in Occam's window and Lucas-Uzawa approach is determined as the most possible growth model to the Iran’s economy.
Conclusion & Suggestions: The main conclusion of this study highlights the agriculture subsection and the non-oil economic section for having better responses to bank credits and showing more growth. Our conclusion was based on a quasi-Bayesian approach because of the lack of the degree of freedom; in adition to the regression that was implemented just for the economy of Iran. Therefore, future studies, to conquer the lack of the degree of freedom, could apply a panel model among resource-based economies and survey the role of financial development, specifically bank credits.

One of the major economists’ interests in the recent decades has been oil and its causes. Oil is one of the key strategic commodities in the world that plays a major role in setting the political and economic relations among countries. The economic structure of the petroleum exporting countrie's dependency on oil revenues causes the affection of global economy in recession boom to economy of such countries. In most oil-exporting countries (e.g. Iran), oil revenues are the government's and state-owned. As the recipient of oil revenues, leads the current and development budgets of these revenues to different economic sectors. To make a good decision and to improve their societies, the governments need to design the budget. To do its functions, a government uses budget as a planning and financial tool. Accordingly, oil price fluctuations have a major influence on the government's spending of oil revenues as a major source for financing different expenditure categories. Iran has a history of more than a century in the exploration and production of oil; the first successful well exploration was Masjid Suleiman on May 26, 1908. Since then, based on the latest oil and gas reports, 145 hydrocarbon fields and 297 oil and gas reservoirs have been discovered in Iran, with many fields having multiple pay zones. Proved oil reserves in Iran, according to the government, ranks as the fifth largest one in the world at approximately 150 billion barrels as in 2014, although it ranks as the third country if Canadian reserves of unconventional oil be excluded. This is roughly 10% of the world's total proven petroleum reserves.
Oil sector in most of the oil exporting countries (such as Iran) is a state-run sector and oil revenues belong to government. Iran is an energy superpower in which the petroleum industry plays an important part. In 2004, Iran produced 5.1 percent of the world’s total crude oil (3.9 million barrels per day), which generated revenues of US$25 billion to US$30 billion and was the country’s primary source of foreign currency. In 2006 levels of production, oil proceeds represented about 18.7 percent of gross domestic product (GDP). However, the importance of the hydrocarbon sector to Iran’s economy has been far greater. The oil and gas industry has been the engine of economic growth, directly affecting public development projects, the government’s annual budget, and most foreign exchange sources. In 2009, the sector accounted for 60 percent of total government revenues and 80 percent of the total annual value of both exports and foreign currency earnings. Oil and gas revenues are affected by the value of crude oil on the international market. It has been estimated that at the Organization of the Petroleum Exporting Countries (OPEC) quota level (December 2004), a one-dollar change in the price of crude oil on the international market would alter Iran’s oil revenues by US$1 billion. In 2006, exports of crude oil totaled 2.5 million bpd, or about 62.5 percent of the country’s crude oil production. The direction of crude oil exports changed after the Revolution because of the U.S. trade embargo on Iran and the marketing strategy of the NIOC. Initially, Iran’s post-revolutionary crude oil export policy was based on foreign currency requirements and the need for long-term preservation of the natural resources. In addition, the government expanded oil trade with other developing countries. While the shares of Europe, Japan, and the United States declined from an average of 87 percent of oil exports before the Revolution to 52 percent in the early 2000s, the share of exports to East Asia (excluding Japan) increased significantly. In addition to crude oil exports, Iran exports oil products. In 2006, it exported 282,000 barrels of oil products, or about 21 percent of its total oil product output. Iran plans to invest a total of $500 billion in the oil sector before 2025. In 2010, Iran, which exports around 2.6 million barrels of crude oil a day, was the second-largest exporter among the Organization of Petroleum Exporting Countries. Several major emerging economies depend on Iranian oil: 10% of South Korea’s, 9% of India’s and 6% of China's oil imports come from Iran. Moreover, Iranian oil makes up 7% of Japan’s and 30% of all Greek oil imports. Iran is also a major oil supplier to Spain and Italy. This study investigates the asymmetric effects of oil price fluctuation on government expenditure based on Mork's (1989) and Hamilton's (1996) definitions. In order that, To this end, the oil prices, total government expenditure, current and development expenditures of the government, per capita total expenditure, per capita current and development expenditures and the deviation of the real exchange rate during the period of 1965 to 2011 have been used within the framework of the vector autoregressive model.
The results indicated that fluctuations in oil prices have asymmetric effects on government expenditure. According to both definitions, oil prices increase relative to oil prices decrease has a greater effect on government spending; however, the effect of oil prices decrease on government spending is more sustainable than oil prices increase. Also, such changes in oil prices rise or fall have more impact on construction costs compared to current expenditures that verifies the stickiness of current expenditures.

Intertwined structures of modern economies make losses to spread from a sector or a country to other countries or economic sectors. Empirical evidence has shown that markets are not isolated from each other and their movements are not separated. Particularly in recent decade’s development of agencies and international and multinational organizations, developments in information technology (IT), deregulation of financial systems in industrialized countries and immense growth in international capital flows have caused financial markets in the world to have more connections (Bracker & Koch, 1999). Volatilities in various asset markets are intensely in linkage. Therefore, it is vital for investors to have the knowledge of the linkages between financial assets to take the appropriate decisions.
Since the beginning of the summer of 2011 Iran's economy has been through a very special situation as a result of sanctions, targeted subsidies effects, increasing liquidity for many years and other factors. After a long period of exchange rate management in this country, instability gripped the market and consequently, the gold coin market was also experiencing an increase of volatility. Tehran Stock Exchange index began to break the records since 2012. Due to the economic recession and high inflation, entering stock market, coin market or the foreign exchange market as investing alternatives to investors who had hot money in their hands.
In such a turmoil that has been created in the aforementioned property market, these questions may be asked that; how is the structure of interrelationships of assets such as stocks, gold coin and currency in Iran? Is there any evidence of the occurrence of the phenomenon of financial contagion in currency, gold coin and stock markets in Iran?
Theoretical Framework: Many theoretical explanations are presented about financial contagion in the financial literature. In most studies, financial contagion is considered as highly co-movements which is the result of rational behavior of activists in markets with failure (e.g., asymmetric information, risk-bearing capacity, imperfect competition, wealth and borrowing constraints, etc. ) or as irrational decisions by the same market activists (e.g., herding behavior) (Choe, et al., 2012).
According to Claessens and Forbes (2004), financial contagion causes has been classified into 2 groups as follow:
Following the study of Choe, et al. (2012), our model consists of three parts: first, we use the dynamic conditional correlation (DCC) model proposed by Engle (2002) to capture the time-varying nature of the conditional correlation. Then, inspired by very restrictive definition if contagion provided by World Bank (contagion is defined as a statistically significant change in the correlation dynamics during "crisis times" relative to correlations during "tranquil times"), to examine contagion, we specify modified DCC multivariate GARCH model, with a time dummy variable imposed for representing the turmoil periods and performs the likelihood ratio (LR) test. Finally our results from time-varying conditional correlation test are compared with the results of adjusted traditional correlation test.
The obtained results of using t-student test for financial contagion hypothesis between markets indicated that the exchange market as the crisis trigger seems to be affected by the gold coin market. Furthermore, the t-student of their adjusted correlation coefficients during the crisis was significant. This result supports a pure contagion hypothesis after the exchange market shock. Also, the likelihood ratio test results indicated that only linkages between two markets (including exchange market and gold coin market) exhibited contagion evidence. The estimated value of was positive and statistically significant at the 5% level, implying that there was a sudden jump in the conditional correlation dynamics between exchange market and gold coin market. Moreover, the results showed that there is no evidence of financial contagion in the relationship between exchange-stock and gold coin-stock markets. The estimated value of was insignificant in the modified DCC model of the exchange-stock and gold coin-stock markets. This implies that there is no significant jump in the conditional correlation dynamics between the exchange market and stock market as well as gold coin market and stock market during the crisis period. This result is consistent with the conventional t-test results.
Conclusion & Suggestions: Following Choe, et al. (2012), we used a time-varying conditional correlation test for financial contagion among exchange, stocks and gold coin markets during the period of 27/03/2010 to 21/09/2013 in Iran. In this time-varying correlation test, contagion is defined as a structural break in the dynamics of conditional correlation during the crisis period. The reason for the selection of this period of time is dramatic fluctuations in the mentioned market (especially in exchange market and gold coin market) whose starting point was 08/2011. Using the dynamic conditional correlation (DCC) model, we found that only the exchange market and gold coin market show evidence of financial contagion.
It is rational that the very high correlation and financial contagion between gold coin and exchange market could actually neutralize the benefits of portfolio diversification. In front, low correlation and lack of financial contagion between stock-exchange markets and stock-gold coin markets nncourages to invest in stock market instead of investing simultaneously in coin and exchange markets in Iran.

The savings becomes to invest in the capital market and then import into the production cycle and helps to the development and growth of countries. However, inefficient capital markets, cause savings to flow into real assets. Gold is a real asset, liquidity with high strength, and a suitable replacement for money. This wealth is a booming market in Iran. Fluctuations in the price of gold in addition to the influence of other markets can also affect other markets. Therefore, it is important for the state and the people to understand the trend in the price of gold and gold coins. The gold price forecast will help policymakers to make the right decisions. On the other hand, it is difficult and complicated to accurately predict the real variables. We need to recognize the structural nature is predictable pattern. In this article, chaos theory was used to identify the structural nature of the time series of Bahar Azadi gold coin.
Theoretical Framework: Chaos theory analysis of the systems that have non-linear relationships and irregular time series. Economic time series variables follow a stochastic process and thus are not predictable. However, the series are not random, and are expected in the short term. There are tests for chaos in time series, such as correlation dimension, BDS, and Lyapunov exponent maximum test. Results of the study by Kim et al. (2003) showed that the BDS test is more efficient than other tests.
For the purpose of this study, the non-linearity of the BDS test, and the Lyapunov exponent maximum test of the chaotic time series were used. BDS test was conducted in three stages: the original data, the residual of ARIMA, and the residual of GARCH. To determine the structure of time series of Bahar Azadi gold coin, 1670 observation was divided into 8 groups of the two hundred. Null hypothesis test is the IID and independent data. The Lyapunov exponent maximum test check on all data. Positive values of the statistics indicated the existence of chaos in the system. R and MATLAB software were used for data analysis.
First, the stationary data were checked. Dickey-Fuller test the null hypothesis is accepted, which implies the existence of a unit root. The first stage of BDS test was performed on the original data in the dimensions inscribed. The results showed that the null hypothesis was rejected, except the first group. As a result, the original data were not IID, and linear or non-linear dependence exists between them. Before the second phase of the test, the appropriate ARIMA model was selected. The unit root test was performed on the residual of ARIMA, and the null hypothesis was rejected. As a result, BDS test was conducted on the residual ARIMA. In the third stage, first the variance heterogeneity was checked, white test the null hypothesis is rejected, thus confirming the heterogeneity of variance. Then, the existence of ARCH effect was checked. ARCH effect in the first five groups, GARCH effect in the next three tests by Ljung-Box and LM-ARCH was confirmed. According to the BDS test conducted on the residual of GARCH, the null hypothesis was rejected, which residual IID, and linear and nonlinear dependence does not exist, thus confirming the process of chaotic time series data structure of Bahar Azadi gold coin. Wolfe algorithm was used in this study to calculate the Lyapunov exponent maximum test. The results showed that the Lyapunov exponent was small and positive for all aspects and intervals.
As a result, time series of Bahar Azadi gold coin is possessed of a chaotic process. So we can predict future prices with the non-linear model in this series.

Identification of factors affecting the money demand plays a very important role in the detection of monetary transmission mechanism; and knowing the elasticity of demand for money is vital to guide the monetary policy. The relationship between real money demand and its determinant factors has been at the center of a considerable amount of economic studies during the last decades. Exchange rate is one of the most important factors that has an important effect on many economic variables such as demand for Money. Apart from exchange rate, the variations in exchange rate that cause exchange rate uncertainty also can influence investor's expectations, the preferences of holding financial assets, and the money demand. With regard to the importance of exchange rate uncertainty and its impact on the demad for money, this paper aims to investigate the effects of exchange rate uncertainty on the money demand in Iran over the period of 1988(1) to 2008(4).
Theoretical Framework: The idea that money demand depends on the exchange rate in addition to income and interest rate was first proposed by Mundell (1963). During the last decades, subsequent studies tried to justify the relationship between exchange rate and money demand. Tower and Willett (1976), Al-khuri and Nsoul (1978), Holden, et al. (1979), Cuddington (1983), Bergstrand and Bundt (1990), Bahmani-Osooee and pourheydarian (1990), Leventakis (1993), Chaisrisawatsuk, et al. (2004), Arshad Khan, and Sajjid (2005), Azim, et al. (2010), and Shahadudheen (2011) emphasized on the role of the exchange rate on money demand. In addition to exchange rate, the variations in foreign exchange rate affect composition of optimal money holding. Changes in exchange rate have two effects on the money demand, i.e., wealth effects and substitution effects. Wealth holders ordinarily evaluate their asset in terms of domestic currency. Exchange rate depreciation, for example, would increase the value of their foreign assets held. To maintain a fixed share of their wealth invested in domestic assets, they will repatriate part of their foreign assets to domestic assets, including domestic currency. Hence, exchange rate depreciation would increase the demand for domestic money. On the other hand, exchange rate variations may cause a currency substitution effect, in which investor's expectation plays a crucial role. If wealth holders expect that the exchange rate is likely to fall further following an initial depreciation, they will respond by raising the share of foreign assets. In this condition exchange rate depreciation means higher opportunity cost of holding domestic money. Therefore, currency substitution can be used to hedge against such risk. In this regard, exchange rate depreciation would decrease the demand for domestic money (Sahadudheen, 2012).
In this study, money demand has been considered as a function of Gross Domestic Product (GDP) and inflation (to represent the economic activity and the opportunity cost of holding money respectively), real exchange rate and real exchange rate uncertainty. The data used in this study was gathered from central bank of Iran over the period of 1988(1)to 2008(4). To assess the relationship between the series, first, real exchange rate uncertainty was calculated by adopting a Generalized Autoregressive Conditional Heteroskedasticity (GARCH ) model and then was included in the money demand function along with other factors such as Gross Domestic Product, real exchange rate and inflation (as a proxy for interest rate). In the next step, with regards to nonstationary variables, cointegration test was performed to estimate the long run demand for money. Results indicated that there is long run relationship between variables in demand for money model function. Finally, the money demand function was estimated by means of Vector error correction Model (VECM).
In this study, we argued that since exchange rate and exchange rate uncertainty have both wealth and substitution effects, they could have a direct impact on the demand for money aside from other variables such as income and inflation. The estimation results from VEC model revealed that income elasticity of money demand (M2) was significant and positive; in the other words, the increase in Gross Domestic Product would increase the money demand. The effect of inflation on money demand was significant and negative. Inflation indicates that the cost of money holding and the increase in it would decrease demand for money. Real exchange rate and real exchange rate uncertainty have had negative and significant effects on money demand that indicates the substitution effect in Iran's economy. In fact, currency substitution effect has been overcomed by the wealth effects.
Conclusion & Suggestions: The negative effects of real exchange rate and its uncertainty on money demand indicates that movement and uncertainty of exchange rate decrease demand for money, which supports the substitution effect. In fact, with the probability of a considerable variation in exchange rate, the opportunity cost of holding money would increase and people prefer to substitute domestic money with foreign currency. For this reason, in order to avoid substantial fluctuations and stabilization of the exchange rate, the adoption of appropriate monetary and foreign exchange policies by the central bank is necessary for Iran's economy.

Targeted Subsidies is an important and visible part of the economic development plan which leads to a change in the subsidy. Factors such as the expansion and diversification of economic activities, the increasing role of government in the development of public services, social security, the development of government commitments in social and economic fields, efforts towards realizing economic growth, and equitable distribution of income have turned the payment and receipt of taxes to an important and effective tool. Taxation is one of the most important levers in the hands of the government which has a major role in preventing inflation and recession as well as in a fair distribution of wealth in the society.
In order to reach the objectives of this study, the following question was raised: Has the targeted subsidies plan affected the combination of taxes?
The statistical population included informed and clear-sighted people about factors affecting the tax income including experts of the tax organization. Statistical sample consisted of 35 persons and the method of sample selection was random sampling. After collecting questionnaires, data analysis was performed using SPSS software package. Given that the studied variable in this research is measured based on an interval scale, the following relations were used to determine the sample size: Where, r is the upper bound of the relative error determined previously, N is the size of population, S and are population’s parameters (error coefficient is and confidence coefficient in level of 99% is equal to ). The required sample size for each group of statistical population was obtained as the table below.
Table1: population and sample sizes
Group Population size Parameter estimation from initial sample Final sample size
mean Standard deviation
Taxes on goods and services 82 3.65 0.72 35
The questionnaire queries the effectiveness of various factors on the amount of tax on goods and services.
Validity of questionnaire
Validity means to what extend the provided tool measures the considered specific concept (Secaran, 2001). The questionnaire used in this research has been validated according to theoretical foundations and the opinion of experts, intendants and advisers .
Reliability of the questionnaire : In order to calculate Cronbach alpha, firstly, it is required that the variance of scores of each subset of questions be calculated and then the total variance be determined. Then by replacing them into the following formula the value of the Cronbach alpha will be obtained: Where, k is the number of subsets of questionnaire or questions of questionnaire; is the variance of kth question and is the total variance.
Thus, Cronbach alpha reflects the amount of positive correlation of the members of a set with each other.
Table2: Cronbach alpha coefficient
Row Questionnaire Cronbach alpha coefficient
1 Taxes on goods and services 0.840
In checking his hypothesis, in order to get familiar with the manner of answers provided to the questionnaire and the significance test of each of them, firstly using frequency tables, we checked the answers. Then using t-student significance test, we checked the mean of answers so that a clear result was obtained about the view of respondents on each question.
Table3. reports the results of evaluating frequency percentage of provided answers to independent variable questions (Taxes on goods and services) (%) row Factor Increase so much increase Do not change decrease Decrease so much
1 Decreased export 0 20 48.6 28.6 2.9
2 Increased import 25.7 42.9 14.3 17.1 0
3 Decreased production (decreased economic growth) 2.9 77.1 0 17.1 2.9
4 Increased prices general level 2.9 77.1 0 1.71 2.9
5 budget deficit (increased government debit, more money issue for supplying government’s expenditure) 2.9 76.5 14.7 5.9 0
*See the rest of the table to text
As observed in the questionnaire, the scores given to each item of the questionnaire are as follow: highly reduces (1), reduces (2), does not change (3), increases (4), highly increases(5). Basically, the value of 3 is considered as the comparison criterion and test of mean comparison. The result of this test is reported in table (4) bellow.
Now, after t-student test and understanding the relationship between research variables, we draw path analysis diagrams to understand the direct and indirect effects of dependent and independent variables. Path analysis of variables studied in this research question can be seen in Graph 1 below.
Graph 1: Path analysis of research variables (Taxes on goods and services)
Totally, in the analysis of trends and the share of proceeds of direct and indirect taxes, it is important that before and after the implementation of the plan, no evident and significant change is observed in the trend of components and the total collected direct tax. In other words, the policy of targeted subsidies, at least in the short term, has had no significant effect on the amount of tax on goods and services proceeds.

Continuous growth and development in the economy need to attend its determinants. Investing or forming capital is the necessary condition for economic growth and development. The place and the role of investing in the mentioned processes are to the extent that investing is the motive engine in the economic growth. Currently, the situation of Iran's economy is in a way that savings and internal sources are not sufficient and attracting foreign capital seems to be the only useful and valid way (Komijani & Abasi, 2006).
The main goal of this study is to evaluate the determinants of inward FDI, particularly volatility of exchange rate in Iran, by using the Johansen-Juselius integration system approach model covering the period of 1980Q2-2012Q4. In this research, the volatility of real exchange rate is obtained by Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) method.
Effective Factors on Volatility of Exchange Rate and Its Relationship to FDI
In theoretical view, Dornbusch (1976) showed that forecasted monetary shocks through overshooting effect of exchange rate could create extreme volatility of exchange rate. In addition, Calderon (2004) maintained that the stability of monetary shocks is the only effective element on the variation of exchange rate and the non-monetary elements including efficiency shocks and state expenses can affect them.
Based on Frenkel and Mussa (1985), the continuous increase of state expenditures leads to a balanced increase of real exchange rate in the long run and consequently to the increase of net foreign equities. Similarly, state expenditures through influencing on the demand side of economy in short run can have a positive effect on real exchange rate.
Cociu (2007) defines interest rate as one of the effective variables on the exchange rate volatility. Based on the macroeconomic subjects, variation in the interest rate leads to variation in inflation and exchange rate. Therefre, it is expected that by the increase of interest rate, the inward foreign investments increase and consequently the local money value increases. The other non-monetary effective variables on the variation of real exchange rate are the efficiency growth.
Cushman (1985) believed that the relation between the variation of exchange rate and FDI flow is in dependent to the place where the data have been purchased, products manufactured, fiscal capital emanated from and products sold.
Econometric Model Specification: By considering most theories and tentative studies for identifying the determinants of inward FDI, emphasizing the economic factors of the country and also specific concerns of Iran’s economy, variables such as exchange rate, GDP, openness, world oil price, volatility of exchange rate can be introduced as the determinants of inward FDI in the econometric pattern below: (1)
Estimating the GARCH Model of Iran's Exchange Rate
The null hypothesis was rejected, showing that…(F=13.79; p-value=0.0003).
The results of the GARCH (1,1) estimated model can be seen as follows. According to the results, the effects of Garch are accepted.
Table 1: Garch Test
Exchange rate auto-regressive model Residuals` variance of exchange rate auto-regressive model
Variables Cofficients Variables Cofficients
c 20.59(0.00) c 428.13(0.06)
1.003(0.00)
1.73(0.00)
-2.09
0.32(0.00)
Source: The research And so we have: Therefore, the volatility of real exchange rate are calculated using GARCH (1, 1) in the above equation.
Investigating the stationary of variables through Dicky Fuller unit root test all of them are static in the first order difference.Therefore, all of the variables are the convergence of degree one, I(1).
Akaike Information Criterion (AIC) and Schwarz Criterion (SC) indicators can be used to determine the optimum lags. According to adjusted LR test, the order of 7 bases on (AIC) index is accepted.
Usually for estimating the coefficients of the model and specifying the long run relationships, we need thetwo statistics of trace and max. Monte Carlo's studies reveal that when the residuals of equations have inordinate skewness or kurtosis, the trace test is more suitable than max test (Noferesti, 1999).
We estimated the regulated standard from conditional form (Pattern 1) to the unconditional one (pattern5). Based on the results of this study, pattern 3 is the proper one for co-integration analysis. Moreover, based on this pattern, the existence of 3 cointegrated vectors is confirmed.
Table (2) shows the coefficients of the cointegrated vectors that are explanatory of long run equilibrium relations between the model variables. Among these vectors, the coefficients of the third cointegrated vector are matched with the economic theories and have the expected signs.
Table 2. Estimated Cointegrated Vectors in Johansen Estimation (in Brackets)
Variables Vector 1 Vector 2 Vector 3 Normalized vector 1 Normalized vector 2 Normalized vector 3
LFDI 0.95 0.11 0.63 -1.00 -1.00 -1.00
LYD -0.40 0.45 -0.13 0.42 4.21 0.21
Os 0.021 -0.025 0.001 -0.02 -0.24 -0.002
Op 0.46 0.26 0.09 -0.49 -2.45 0.15
Se 0.00006 0.00006 0.0008 0.00006 -0.005 -0.001
E 0.0001 0.0001 0.00006 0.0001 0.001 0.0001
Source: The research Therefore, based on vector 3, we can express the long run relationship between the variables as below.
LFDI = 0.21 LYD - 0.002 OS .15 OP - 0.001SE .0001 E (3)
The estimated result shows that, based on the theoretical basis, FDI in Iran has a direct relation with GDP, openness and exchange rate variables and has a negative relationship with the volatility of exchange rate and world oil price.
The IRF and FEVD tests confirm the estimated results of the long run relationship quite well. As a result, the occurrence of one shock in GDP, openness and exchange rate have a significant and positive effect on FDI.
Empirical results show that openness, GDP, and exchange rate do have a significant and positive impact but volatility of exchange rate and world crude oil prices do have a significant and negative impact on the flow of inward FDI in Iran. Therefore, economical politicians should minimize the barriers of import through joining the world trade organization; that is, through de-regulation and reduction of tariffs, and at the same time, emphasizing production and non-oil exports especially industrial commodity, and promoting the foreign trade. For the world oil price, it is recommended that the goals of macro-economic policies support the base and power of country production to increase the non-oil export and reduce the country's dependence on the world crude oil price. For the GDP, increasing the efficiency of internal resources and activating the non-used capacity to absorb more FDI.

Privatization is an important process in organizational change. If properly implemented, it will lead to good economic growth; for this reason, it is known as the economic engine (Pheko, 2013). If not implemented properly, or if implemented neglectfully, it will lead to undesirable results. Therefore, privatization is an important issue in the economic development of countries and ignoring it causes irreparable damages to the body of society (Zahedi, 2012) & Mirkamali). On the other hand, the main causes of privatization failure in financial institutions are lack of attention to the prerequisites of the privatization process (Al-Omran& Al-Omran, 2011), dimensions of privatization (Waigama, 2008), and types of privatizations (Clifford, 1993).
Theoretical Framework: Privatization was raised for the first time in the theories of classical economists (Waigama, 2008). It refers to the change of control or ownership from the public system to the private system (Al-Omran, et al., 2011; Mirzade, Shahbazi, & Javahery, 2009).
Privatization is the most important component of the world economy in the 21st century (Sawagvudcharee, 2012). Therefore, prescribing privatization programs for developing countries is the only way to treat their sick governmental economy (Mirzade, et al, 2008; Lashkari, et al., 2009).
Privatization goals include: transforming the economy to a dynamic, competitive and developmental economy and reducing the government tenure (Ram, 2012); increasing international competition (Clifford, 1993), deregulating and improving productivity (Abdel Shahid, 2002) , reducing the scope of direct jurisdiction of government in the economy (Waigama, 2008; Clifford, 1993), increasing the capacity and entrepreneurial skills and the economic enterprises efficiency (Pheko, 2013; Clifford, 1993), capital market the development (Waigama, 2008; Clifford, 1993) and gaining access to new technology and foreign markets (Clifford, 1993).
Privatization occurs in five states: privatization of responsibilities, i.e., removing the monitoring role of government from a specific part; privatization of ownership, i.e., transferring the majority or minority shares to the private sector; privatization of operations, i.e., cooperation between government and private companies (Mirzade, et al, 2009); and -tickle downs to the poorest, i.e., the development of public infrastructure, helping public companies through investments and tenders by contractors (Pheko, 2013).
In organizational change, noteworthy models are : the force field analysis (Kurt Lewin, 1951), System Analysis (Likert, 1967), the six box model (Weisbord, 1976), the adaptation analysis of organization model (Nadler & Nachmn, 1977), the 7S framework (McKenzie, 1981), TPS framework (Tichy, 1983), high-performance planning (Nelson & Brown, 1984), recognizing individual and group behavior (Harrison, 1987), and Burk-litwin model (1982). Among these models, Burk-litwin-is the most comprehensive model of organizational change (Pheko, 2013).
Research This study is practical in terms of its nature and goal, and is descriptive in terms of data collection as well as a survey and a libarary-based one. 323 questionnaires were distributed among the staff of privatized banks and the analysis was done based on the 323 questionnaires. 7 variables were applied for hypothesis testing. To measure the dimentions of Burk-litwin model, standardized questionnaire (Zhibin, 2004) was used and the variables of return on assets ratio (ROA) and return on equity ratio (ROE) were measured for private banks of Saman, Parsian, Pasargad, Eqtesad Novin and privatized banks of Tejarat, Saderat, and Mellat.
The results showed that 7 dimensions of Burk-litwin model, i.e., change in leadership, culture, structure, strategy, management, policy and decsion making, and motivation were used less in privatized financial institutions. This result is consistent with works of Proskat (1978), Mills and Snow (1978), Gordon (1985), Chandler (1962), Mills at al. (1978), Trigo and Zimerman (1980), and Bagheri, et al. (2011).
Furthermore, the results of this study are in line with the results of studies by Joyce and Slocom (1984), Ouchi (1977), and Galbraith (1977) that examine the dimensions of structure, atmosphere, management practices, systems and job requirements; Schneider (1980) and Schneider and Bourne (1985) that examine the dimensions of management practices and dominated atmosphere; Hammer (1988), Zuboff (1988), Jordan (1986), Ezazi, et al. (2011), and Mirkamali, et al. (2012) that examine the dimensions of systems, atmosphere, management practices, individual needs and values.
Among the dimensions of financial institutions, management dimension was at the lowest level. Therefore, the management aspect has more effect on privatized financial institutions than other factors. The culture dimension has less effect on financial ratios of privatized financial institutions than other factors. Not using the dimensions of Burk-litwin model decreases the return on assets ratio (ROA) in the privatized financial institutions rather than initially private institutions. Moreover, not using dimensions of Burk-litwin model reduces the return on equity ratio (ROE) for privatized financial institutions rather than initially private institutions, and not using dimensions of Burk-litwin model increases the ratio of public, administrative and organizational cost to the total cost of company in the privatized financial institutions rather than initially private institutions.
Management dimension is at the lowest level among the dimensions of Burk- litwin; thus, this factor has more effect on financial ratios of privatized institutions than other factors. It is suggested that managers of financial institutions acquire expertise education, and skills for the successful implementation of privatization. To train skilled staff, we should empowerthem with a high commitment to change through trust-building, capacity-building and accountability in their organizations, and to providethem with a culture that is open to organizational change. Given the importance of the organizational change, in order to survive in the current extensive competitive scene and with regard to the high potential of the country for research in this area, it is suggested that the causes of success and failure for the phenomenon of organizational change be scrutinized in other organizations, so that, the reliability of the results of this research can be tested in other cases.

Lending is a principal business activity for most commercial banks. The loan portfolio is typically the largest asset and the predominant source of revenue. As a result, it is one of the greatest sources of risk to a bank’s safety and soundness.
Credit risk is the risk of loss due to a debtor's non-payment of a loan or other line of credit (either the principal or interest (coupon) or both). Defaulting occurs when a debtor has not fulfilled his or her legal obligations according to the debt contract, or has violated a loan covenant (condition) of the debt contract, which might occur with all debt obligations including bonds, mortgages, loans, and promissory notes. Since financial innovation and derivatives grow rapidly in competitive financial industry, credit risk measurement and management become essentially important.
Credit risk is the primary financial risk in the banking system and exists in virtually all income-producing activities. How a bank selects and manages its credit risk is critically important to its performance over time; indeed, capital depletion through loan losses has been the proximate cause of most institution failures. Identifying and rating credit risk is the essential first step in managing it effectively.
Well-managed credit risk rating systems promote bank safety and soundness by facilitating informed decision making. Rating systems measure credit risk and differentiate individual credits and groups of credits by the risk they pose. This allows bank management and examiners to monitor changes and trends in risk levels. The process also allows bank management to manage risk to optimize returns.
The consistent use of analytic credit risk has many advantages. It improves an institution’s risk assessment time, speed, accuracy, consistency, bad debt reduction and prioritization of collections. Using analytic credit scoring, an institution can review its entire receivable portfolios in the same time as it would take to review just one account by traditional methods. Analytic credit risk assures accuracy since the review process is mostly free of human error. It offers consistency, by using the same set of rules and weighted variables over the entire portfolio. Scoring permits regular reviews of the entire account base, thereby, quickly and efficiently identifying accounts that require immediate attention, and isolating customers who warrant human intervention. The net effect is a substantial reduction in risk assessment time and a more systematic approach to collection.This paper examines the factors affecting the credit risk of real customers of Tejarat bank of Neka.The data that is used in this paper was extracted from 2545 loan files of real customers of Tejarat Bank of Neka who had got credit facilities during the years 1381 to 1390, and logistic regression was used to evaluate the data.
In this study, first we introduced the factors affecting credit risk of real customers of Tejarat bank and then defined the risk and presented the methods that can measure the risk. Then we extracted the data and used the Eviews software to estimate our model and finally analyzed the results.
The statistical techniques used in this research are a logit method to estimate the probability functions. Logit model is one of the easiest statistical modelsthat is based on the analysis logit model (logistic), the financial ratios and other quantitative and qualitative variables for predicting the risk of non-repayment of loans are used. In this model, the probability of failure is showed in normal distribution: This function is ideal for all levels of Z values between zero and one.
Variables include the dependent variable (including 2 cases: will the real customers pay or not pay their loan) and independent variables (including 6 variables that have an impact on the repayment).One of these independent variables is the period of repayment of loans or facilities and another one is the amount of loans that customers have got and also the rate of interest of loans and also collateral types that is got for thoes loans and two final variabels are mandatory or non-mandatory loans variable and loan types variable.We can see all these results in table below:
The result of this study shows that loans repayment period, interest rates of loans, and collateral types have significant effect on the probability of default; but mandatory or non-mandatory loans and loan amount do not have significant effect on the probability of default. Despite the significance of the coefficients, according to the sign of the coefficients, as expected, we found that the probability of default is reduced by increasing the loan repayment period and with a decrease in interest rates getting banking deposits as collateral for loans have the greatest negative impact on loan default regarding the loan types, Gharz-ol- Hasaneh and Mosharekat loans have the highest and lowest impacts on default possibility.

Extended Abstract: As one of the most important and basic economic development tools, the targeted subsidies plan (reforming relative prices system of subsidized revenue) can play a major role in clarifying and making competitive the business environment, increasing the efficiency of government programs and policies, modifying consumption patterns, efficient allocation of resources and facilities and production factors, establishing the justice in the distribution of government support, etc. Thus, selection and determination of qualified individuals and target groups is a necessary condition for target attainment.
For the purpose of the study, the following question was posed: Whether the targeted subsidies plan could affect the combination of tax?
The statistical population included aware and clear sighted people about factors affecting tax income including experts of the tax organization. The statistical sample consisted of 35 persons and the method of sample selection was random sampling. After collecting questionnaires, data analysis was performed using SPSS software package. Given that the studied variable in this research is measured based on an interval scale, the following relations may be used to determine the sample size: Where, r is the upper bound of the relative error determined previously, N is the size of population, S and are population’s parameters (error coefficient is and confidence coefficient in level of 99% is equal to ). The required sample size for each group of statistical population was obtained as the table below.
Table1: population and sample sizes
Group Population size Parameter estimation from initial sample Final sample size
mean Standard deviation
Taxes on revenues 35 3.14 0.92 28
The questionnaire queried the effectiveness of various factors on the amount of tax on revenue.
Validity of the Questionnaire: Validity means that the provided tool to what extent measure the considered specific concept (Secaran, 2001). The questionnaire used in this research has been validated according to theoretical foundations and the opinion of experts, intendants and advisers.
Reliabilityof the QuestionnaireIn order to calculate Cronbach alpha, it is firstly required that the variance of scores of each subset of questions be calculated and then the total variance be determined. Then by replacing them into the following formula the value of the Cronbach alpha will be obtained: Where, k is the number of subsets of questionnaire or questions of questionnaire; is the variance of kth question and is the total variance.
Thus, Cronbach alpha reflected the amount of positive correlation of the members of a set with eachother
Table2: Cronbach alpha coefficient
Row Questionnaire Cronbach alpha coefficient
1 Taxes on revenues 0.818
In order to get familiar with the manner of answers provided to the questionnaire and the significance level of each test, we firstly, using frequency tables, checked the answers and then, using t-student significance test, checked the mean of answers so that a clear result was obtained about the view of respondents on each question.
Table3. reports the results of evaluating frequency percentage of provided answers to independent variable questions (Taxes on revenue)
(%)
row Factor Increase so much increase Do not change decrease Decrease so much
1 Decreased export 3.6 7.1 35.7 53.6 0
2 Increased import 7.4 40.7 18.5 29.6 3.7
3 Decreased production 0 7.4 3.7 63 25.9
4 Increased prices general level 7.7 38.5 19.2 26.9 7.7
5 budget deficit 17.9 28.6 17.9 28.9 7.1
6 Income redistribution 7.7 57.7 19.2 11.5 3.8
7 Economic non-stability 10.7 14.3 10.7 46.4 17.9
8 Increased interest rate 11.5 15.4 15.4 42.3 15.4
9 International sanctions 10.7 14.3 10.7 39.3 25
10 Decreased sale revenues 10.7 3.6 7.1 57.1 21.4
11 Increased costs (the cost of production inputs and distribution costs) 7.1 10.7 10.7 53.6 17.9
12 Tendency to non-productive and non-standard jobs and activities (the growth of non-formal economy) 3.6 21.4 17.9 42.9 14.3
13 The existence of bargaining opportunity for unions 3.6 32.1 17.9 46.4 0
14 The lack of trust on government and public sector and organization’s performance 0 11.1 11.1 59.3 18.5
15 The false culture of tax evasion 0 11.1 11.1 55.6 22.2
16 Lack of declaring real incomes by the taxpayer at the time of diagnosis 0 0 7.7 61.5 30.8
17 Simplification of the financial system (with emphasis on reducing compliance costs and encouraging self-declaration) 21.4 50 17.9 10.7 0
18 shortage of skilled labor 0 0 14.3 60.7 25
19 Lack of transparency and aggregating the information of the taxpayer 0 3.6 10.7 71.4 14.3
20 Mechanization of declaration, detection and collection 17.9 71.4 .3.6 7.1 0
21 Lack of information flow from the tax organization to taxpayers 0 11.1 29.6 59.3 0
22 Lack of financial and judicial support for organization’s personnel 0 3.7 3.1 70.4 25.9
23 Organizational features 0 11.1 6.3 74.1 14.8.
24 Unsuitable performance of tax auditors 0 3.8 11.5 69.2 15.4
25 Weakness of regulations, circulars and instructions 0 3.8 7.7 57.7 30.8
26 The existence of broad legal exemptions 0 3.6 7.1 53.6 35.7
27 Failure to extend the value added tax system 5.7 0 8.6 18.6 17.1
28 On the head detection method 8.6 34.3 25.7 28.6 2.9
29 Introducing a new tax base (such as value added, total income, so on) 17.1 51.4 25.7 5.7 0
Now, after t-student test and understanding the relationship between research variables, we draw path analysis diagrams to understand the direct and indirect effects of dependent and independent variables. Path analysis of variables studied in this research question can be seen in Graph 1 below.
Graph 1: Path analysis of research variables (Taxes on revenue)