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In the late 1990s and early 21 century there has been a very big rise in the price of housing in most countries including the United States and Japan. The same trend occurred in Iran in the years after the Iran-Iraq war and has continued up to the present. Housing in Iran has always had a mutual relationship with economic fluctuations including monetary policies. Thus the impact of housing on Iran’s economy is significant. In this applied- analytical research, the aim is to investigate the monetary transmission mechanism in the housing market in Iran during the years 1988-2006 by using a SVAR eight- variable model and seasonal data. The research found that the expansionary-monetary shock policy, which is the result of liquidity shock, has had a significant impact on housing prices lasting for 3 years at a time. Meanwhile, the price of housing explains about 20 per cent of the GDP changes in the study period.

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The aim of this paper is to consider the effects of monetary policy on production and prices through asset price channel (the housing price index) in Iran during 1368Q1 to 1387Q4. By Vector Error Correction (VEC) Model, the effect of monetary policy has been considered through this channel. In general, the results show that the debt of banks to the central bank as instruments of monetary policy through the housing price index, at least in the short run could increase the production level and decreases prices. Thus central bank with given facilities to banks can directly and through the housing price index strengthen production level and control prices in the short run. Also we found that shock of the required reserve ratio in general, directly affects production levels and don’t have any effect on production level and prices through the housing price index. Therefore, in using of these tool as instruments of monetary policy, the housing price index channel in monetary transmission policy, has a little effect and only on the production.

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With regard to importance of investment as an engine of economic growth many economists such as Wicksel, Keynse and Harrod believe that investment is the main source of business cycles. Hence this study specifies investment function according to a basic macroeconomic model such as Ramsey model. Application of Ramsey model can help to extend macroeconomics with micro foundations in economy of Iran and prepares new scopes for researchers. The main idea of this study is specification of investment function according to Ramsey model and its estimation by cointegration technique for period (1990:Q1-2007:Q4). The result of econometric estimation indicated a long run relationship between investment, capital stock, and shadow price of capital, installation cost of capital, capital price and terms of trade. Results showed that capital stock, shadow price of capital and terms of trade have direct effects and, installation cost of capital and capital price have inverse effect on investment.

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The optimal usage of oil as a natural resource is an important problem in exporting countries. These countries always are encountered with uncertainty and volatility of oil prices and its effects on real exchange rate. The main purpose of this paper is to investigate the relationship of between oil prices and exchange rate by emphasizing institutional quality in during 1995-2006. The model of this paper is estimated by panel data approach. Findings show that the oil prices have a positive effect on real exchange rate and it reduces international competition power. But institutional quality affects the extent to which the real exchange rates of oil-exporting countries co-move with the oil price. The results show that countries with high institutional quality such as control of corruption and regularity quality have real exchange rates which co-move less with the oil price.

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Value Added Tax (VAT) as a method of tax charging with creating a new tax base broad has been interest of many countries. Also Value Added Tax in our country in order to reform the structure of tax and increasing government revenues was approved by the House after a relatively long time and in the second quarter of year 1387 was carried out. Given that this law as an experiment and for five year was carried out study of effects of this tax on macroeconomic variables is of particular importance. In this study has tried to use the analytical relationships Input-Output and production of technical coefficient matrix constant assumption of fixed economic conditions and economic variables and limiting assumptions of this study to the price effects resulting from applying VAT on cost of whole different departments to deal with the country's economy. By using of model price of input - output and applying tax rates issue of Article 12 on exemption for goods and services and ultimately applying export exemption issue of Article 13 on value added tax the price effects of each section of economy is calculated and with and with considering of share of each section from the whole output the price effects is calculated. Results show that implementation of VAT has had very low price effects.

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The crude oil is both a commodity and a financial asset. As there are many factors affecting the crude oil spot and futures markets, the analysis of the relationship between major factors of these markets is complicated. The main objective of this paper is to investigate the relationship between the price of crude oil in spot and futures market and identify the effect of the crude oil inventory and the interest-adjusted basis risk on these price changes. The monthly data of WTI spot and futures prices, WTI crude oil inventory and interest-adjusted basis risk are from EIA (Energy Information Administration) database. The data period is from January 1986 to December 2010. Due to the unpredictable volatilities and uncertainties in variables, the GARCH error process models are used. Empirical results show that there is a positive, strong and significant relationship between the spot crude oil price changes and futures prices. Additionally, the basis risk changes can affect the spot and futures crude oil prices up to three lags. Also, crude oil inventory changes have a negative effect on the spot crude oil price changes with one lag.

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This paper aims to determine the optimum price of electricity during restructuring process. We maximized social welfare function subject to market equilibrium, maximum production capacity of each group of power plants, maximum demand of each consumer type and the potential of electricity export and import. The model was run using 2007 monthly and annual data by means of GAMS optimization software. The calculated electricity shadow price for the year 2007 was 371.2 per KWh. To get more exact results we run the model for each month separately. The results show that the price of electricity in spring and summer is lower than fall and winter, for marginal cost of power provision in winter rises. This is due to substitution of gas by gasoil and other liquid fuels and also reduction of hydropower production. For both intervals the actual price has a significant deviation from optimum price in Iranian power market.

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One of the most important aspects of vulnerability of the Iran economy can be observed in depreciation of real exchange rate during the oil booms. This phenomenon is called "Dutch disease". In other words when a country starts exporting natural resources, the ensuing capital inflows lead to an increase in demand. The real exchange rate (RER) typically appreciates due to “spending effect” as the price of domestic nontradables increases relative to the price of tradables. The main objective of this paper is to examine the cyclical patterns of the house price and macroeconomic variables in Iran. Using Hodrick and Prescot filtering method, the cross-correlation analysis is first presented to identify the long-run behavior of the variables. Then based on the vector autoregressive (VAR) model, we investigate the interaction between housing price cycles and cyclical component of real oil revenue, real exchange rate, real GDP, money supply and interest rate. The results show that positive oil shocks, leads to an increase in housing price cycles.

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  This paper investigates the relationship between real exchange rate uncertainty and stock price index in Tehran stock exchange for the period of 1995-2009 by using monthly data and applying Bivariate Generalized Autoregressive Conditional Heteroskedasticity model (Bivariate GARCH). The results show that there is a negative and significant relationship between real exchange rate uncertainty and stock price index. However, the relationship between stock price uncertainty and real exchange rate is insignificant. Therefore, our results recommend that the policies which cause more volatility in the exchange market and also more volatility in the real exchange rate should be avoided to ensure the sustainable growth of the stock market and its price index.

  

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A standard assumption in the empirical literature is that exchange rate pass-through is both linear and symmetric. This implies that size (large-versus-small exchange rate changes) and direction (currency appreciations-versus-depreciations) have similar effects. In this paper these assumptions have investigated for Iran's export prices. So, this paper examines the asymmetric exchange rate pass-through to the monthly import price index in Iran during 1997:1–2010:9. Therefore positive and negative exchange rate shocks have been separated using Mork Criteria and large and small exchange rate changes by determining a threshold. The results show that the response of export prices to currency appreciation and depreciation is asymmetric. So, the negative exchange rate shocks have a greater effect on the export prices than the positive exchange rate shocks. According to our estimation results, there is a threshold at 1.3% of monthly changes in exchange rate of Iran and also export prices react asymmetrically to exchange rate at around this threshold. If both direction and size effects are considered, we find that export prices respond asymmetrically to large and small appreciations and depreciations.

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Dominance of arid and semiarid climate in a vast area of Iran along with the water consumption growth necessitates a more sophisticated planning, a more efficient operation towards an optimal allocation and conservation of water resources in the country. In recent decades many countries, including Iran, have adopted increasing block tariffs for domestic water management. This policy is based on a progressive tariffs applied to control and manage the residential water consumption. In this paper, we developed a panel data model to investigate the impact of increasing block pricing on the residential water consumption during 2004-2008. The average and marginal price models of demand for residential water have been estimated to examine the effects of households’ income and the climate conditions on the residential water consumption. Results show that the increasing block pricing system has not efficiently controlled the residential water consumption in Iran.

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Price shocks lead to oil price volatility in world oil markets. In response to this volatility, economic growth may take different regime and behavior patterns in different situation. Investigating this multi behavior patterns can be useful for policymakers to reduce the effect of oil price volatility. In this study, an EGARCH model has developed using the seasonal data of OPEC oil basket nominal prices during 1367:Q1-1389:Q4. Markov switching models is also applied to investigate the multi behavior patterns of economic growth in response to oil price volatility in Iran. The results show that positive oil price shocks sharply lead to formation of oil price volatility, but, the negative price shocks will slightly reduce oil price volatility. Iranian economic growth is affected by this volatility under three different behavior regimes. If the economy switch to one of the regimes (low, medium, high economic growth), the probability of transition between these regimes and their duration is different. So, oil price volatility as a reason for low economic growth in Iran may cause the economy switch to its lower situation.

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From technology, security and physical points of view automobiles are different regarding Hedonic price model, price of a car is a set of implicit prices concerning different characteristics. In this article we estimate the Hedonic price models using 2009 Iranian automobile market data concerning different characteristics for small cars (Engine capacity of 2000cc and less) and large cars (Engine capacity of more than 2000cc): in Iran cars with engine capacity of 2000cc and less benefits from special petrol subsidies while this is not the care for the large cars. The results indicate that characteristics such as antilock brakes (including ABS and EBD), width, airbag, fuel consumption and engine capacity for small cars, variables such as airbag, antilock brakes(including ABS and EBD), and highs for the large cars are important from the points of view of Iranian customers and have significant effects on the car price.

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ABSTRACT Considering the major impact which changes in the real exchange rate and crude oil prices have on various sectors of Iran's economy and the importance of the financial markets role in economic growth and development, this paper aimed to investigate the effects of the changes in real exchange rate and crude oil prices on Tehran stock exchange using the Markov-Switching's nonlinear models. To this end, daily data which belonged to the following periods were used: 20:03: 2005 - 13:10:2010 The result of the estimations obtained through the Markov Switching Models indicated that MSIAH model with two regimes out of the various MS model are the most suitable ones. The result of the research showed that the changes in the exogenous variable of real exchange rate and the crude oil price have lagging positive effect on the Stock Exchange Index. Moreover, the effects of these changes with two lagging time intervals are significant and negative. The practical implications of these findings could be beneficial to the investors and policy makers who need to be aware of the exact nature of the effects which changes in the exchange rate and crude oil prices have on the stock exchange index.

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Developing models for accurate natural gas spot price forecasting is critical because these forecasts are useful in determining a range of regulatory decisions covering both supply and demand of natural gas or for market participants. A price forecasting modeler needs to use trial and error to build mathematical models (such as ANN) for different input combinations. This is very time consuming since the modeler needs to calibrate and test different model structures with all the likely input combinations. In addition, there is no guidance about how many data points should be used in the calibration and what accuracy the best model is able to achieve. In this study, the Gamma Test has been used for the first time as a mathematically nonparametric nonlinear smooth modeling tool to choose the best input combination before calibrating and testing models. Then, several nonlinear models have been developed efficiently with the aid of the Gamma test, including regression models Local Linear Regression (LLR), Dynamic Local Linear Regression (DLLR) and Artificial Neural Networks (ANN) models. We used daily, weekly and monthly spot prices in Henry Hub from Jan 7, 1997 to Mar 20, 2012 for modeling and forecasting. Comparing the results of regression models show that DLLR model yields higher correlation coefficient and lower MSError than LLR and will make steadily better predictions. The calibrated ANN models specify the shorter the period of forecasting, the more accurate results will be. Therefore, the forecasting model of daily spot prices with ANN can interpret a fine view. Moreover, the ANN models have superior performance compared with LLR and DLLR. Although ANN models present a close up view and a high accuracy of natural gas spot price trend forecasting in different timescales, its ability in forecasting price shocks of the market is not notable.

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Abstract

 Forecasting of crude oil price plays a crucial role in optimization of production, marketing and market strategies. Furthermore, it plays a significant role in government’s policies, because the government sets and implements its policies not only according to the current situation but also according to short run and long run predictions of important economic variables like oil price. The main purpose of this study is modeling and forecasting spot oil price of Iran by using GARCH model and A Gravitational Search Algorithm. Performed forecasts of this study are based in static and out-of-sample forecasting and each subseries data is divided in to two parts: data for estimation and data for forecasting. The forecast horizon is next leading period and its length is one month. In this study the selected models for forecasting spot oil of Iran are GARCH(2,1) and a Cobb Douglas function which is functional of prices of 5 days ago. Finally, the performances of these models are compared. For comparison of these models MSE, RMSE, MAE, and MAPE criteria are used and the results indicate that except in MAPE criterion, the mentioned criteria are smaller for GARCH model in comparison to GSA algorithm.

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Energy carrier’s subsidization has placed a significant pressure on government budget in Iran thus, energy price increase is performed in order to ameliorate this case. One of the main challenges that policymakers need to consider is the impact of energy prices increase on the labor market especially, when the national unemployment rate is high. This paper utilizes a computable general equilibrium model based on a Micro Consistent Matrix for 2006 in order to evaluate the impact of energy price increase on the Iranian labor market during 2006. The empirical results are based on two scenarios: Baseline and FOB price increase scenarios. They show that the activity level and demand for labor in “crude oil, natural gas, and coal” as well as “other services” sectors will increase in short-run while the energy carriers’ prices increase. However, in long-run, the labor increment will be lower. Furthermore, the model results indicate that in short-run, the activity level and demand for labor in the other sectors will decrease. On the other hand, the policy will result in a larger decrement in the activity level and demand for labor in these sectors in long-run.

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The fluctuations in the oil price with uncertainty, as an exogenous variable, is the most important factor affecting the fluctuations in the GDP of the countries especially OPEC. This study examines the effect of oil price uncertainty on the Iran’s GDP growth using the seasonal data for the period 1988(1)-2011(4). The model used in this study is the asymmetric VARMA, MVGARCH-M and the estimated method is quasi maximum likelihood (QML). The results indicated that there is a negative and significant relationship between oil price and economic growth over the period. Furthermore, the results show that the conditional variance-covariance process underlying output growth and change in oil price exhibits non-diagonality and asymmetry.

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Hybrid sticky price model is one of  the main models, used to analyze the Persistencyand inertia in inflation. In recent years, Mankiw and Reis (2002),s sticky information model, has also been considered by many economic analysts. So, in present paper, we try to investigate and compare these models by using a Dynamic Stochastic General Equilibrium (DSGE) framework, based on new Keynesian structure. For this purpose, the data 1370:1-1391:4 Iran's economy has been used. The results of the estimated coefficient of inflation inertia indicate, inflation inertia in the model of hybrid price stickiness is more than information stickiness model. Inflation Persistency analysis is based on comparing the autocorrelation function of the original data and simulated data, show that hybrid price stickiness is better thaninformation stickiness model shows inflation persistence.It seems to be a hybrid price stickiness model more consistent with the economy of Iran and economic policy makers can be more confident of the results of this model to use them.

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This paper aims at estimating Geometric Brownian Motion (GBM) Model, based on two central parameters in this model (volatility and drift), and forecasting Henry Hub natural gas daily spot prices (07/01/1997-20/03/2012). Researches reveal that two mentioned parameters estimation can be satisfied with different approaches and in various time scales. Therefore, two approaches of backward looking and forward looking have been used in different time scales and sub-periods. Results show that the volatility and drift values are highly dependent on the time scale and backward results are lower than the forward ones. Moreover, along with increasing the number of random runs of the model although the fluctuating range decreases, the predicted line slope is very close to the actual line. Ultimately, the performance evaluation criteria yields that forward method, clearly in 2009, has the best performance. The sub-periods of 2001-2004 in backward and forward methods have the next best performances, respectively. These sub-periods can be used as a basis for calculating the central parameters of the model. In addition, the results suggest that relying on data used in the most recent period is not sufficiently accurate. Also, it is observed that sub-periods or time scales with higher volatility show better performance evaluation criteria, therefore they can be applied in price forecasting with GBM model.