The effects of oil price shocks on real GDP in Iran

In this paper, the asymmetric effects of oil price shocks on GDP have been investigated by cointegration analysis in Iran economy during the period 1960-2010. We used Hodrick-Prescott filtering to separate positive shocks from negative shocks. The results showed that in long run the negative shocks have stronger effects on output than positive ones that can have damaging repercussions on economic growth. The findings have practical policy implications for decision makers in the area of macroeconomic planning. The use of stabilization and savings funds and diversification of the real sector seems crucial to minimize the harmful effects of oil booms and busts.


INTRODUCTION
Oil production usually accounts for a large share of the GDP of oil-exporting countries and oil price increases directly increase the country's currency value (total oil production increases because the value of oil production increases: the income effect).However, the total effect of oil price shocks on economic performance mostly depends on what the oil producers (mostly governments) do with this additional revenue.High oil prices increase real national income through higher export earnings (Kornonen and Juurikkala, 2007).As a result, wealth will be transferred from oil-importing countries to oil-exporting countries, leading to greater purchasing power for economic agents of oil-exporting countries (Hakan et al., 2010).
Oil price is very instable.Instability is very costly, as economies and budgets adjust asymmetrically (Mehrara and Oskoui, 2007).Oil price fluctuations are a major source of disturbance for the economies of oil-exporting countries given the relative importance of the oil sector in production and exports and uncertainty in the world oil markets (Mehrara, 2008;Behbudi et al., 2010).
Oil revenue is the major part of government income and it recently has played an important role in reimbursing government expenditures in Iran.The Iranian economy is heavily dependent on oil revenues, with about 15% of nominal GDP originating in the oil sector during the period 2000 to 2009.Moreover about 50% of the government's revenues and 70-75% of exports are derived from the oil sector (IMF, Country Reports; Mehrara et al., 2010).
Due to the high dependence on oil revenues, oil price fluctuations have a special impact on the Iranian economy.Oil and gas incomes have a strategic role in the structure of the Iranian economy.Iran's economy relies heavily on crude oil export revenues.The slow growth of Iran's economy is remarkable, since Iran possesses large reserves of fossil fuels like oil and natural gas.One would expect that export revenues of these natural resources should give Iran an advantage over countries without abundant natural resources.Since Iran failed to use this advantage to boost its economic development, it is outperformed by many countries with less favorable conditions for economic development.
Although the topic is the same for oil exporting and importing countries, theoretical model and effecting mechanisms in oil exporting countries are completely different from those in oil importing countries.This paper tend to grow slower than resource-scarce countries.The litera-ture offers six candidate explanations for the resource curse effect: Dutch disease, governance, conflict, excessve borrowing, inequality, and volatility (Devlin and Lewin, 2004;Mehrara, 2009;Mehrara et al., 2008;Mehrara and Oskui, 2007;Gaskari et al., 2005).
The oil price volatility can be transmitted to the economy through the large fluctuations in government revenues.The uncertainty about future oil revenues and the variability of such revenues would result in changes in spending.There-fore, the resulting pro-cyclicality of government spending can ultimately lower growth rates.Carefully looking into some of the potential expenditure mechanisms, one can identify the following (Anshasy et al., 2006).
A positive revenue shock that is perceived as permanent typically leads to higher government spending, especially on non-tradable, creating incentives to shifting resources away from the (non-oil) tradable sector to the non-tradable sector.Such resource movements would lead to higher un-employment, output losses, and ultimately the de-industrialization of the economy; a phenomenon known as the "Dutch disease" (Anshasy et al., 2006).In an oil-dependent economy, the variability of the oil rent will, in the absence of counter measures, spill over into the real exchange rate.An oil price boom will lead to a real appreciation and a decline in non-oil exports.This is often taken as the main symptom of the Dutch disease, but is not in and of itself a cause of reduced welfare (Mehrara and Oskoui, 2007;Mehrara, 2009;Mehrara and Sarem, 2009).
Government budget and expenditures are one of the most important channels through which oil shocks affect aggregate demand, and without devising some mechanisms to stabilize government budgets, oil shocks would have serious effects on government budgets.One of the important reasons for asymmetric effect of positive and negative oil price shock on economic growth is related to the major role of government investments in oil exporting countries and the way it responds to these shocks.When a positive shock occurs, the welfare and consumption expenditure as well as less productive investments rapidly increase.Increase in government expenditures will lead to decrease in quality of spending and economic efficiency, increase in unfinished projects, and rent seeking (Ricardo and Roberto, 2002;Mehrara and Oskoui, 2007;Delavari et al., 2008).If a positive shock is perceived as temporary, accumulating the budgetary surpluses in developing economies is politically unpopular and the government will be subject to pressures to increase spending, especially on public projects.Many studies found that most of the large surges in public capital spending during boom times are non-productive and typically have a very low return (Talvi and Vegh, 2000;Anshasy et al., 2006).
But when a negative shock occurs, long term investments and economic activities shrink first, due to non-refunding of a major part of a productive spending with useful impacts on growth, and immediate decrease of intermediate and capital imports.Negative oil shocks might be responsible for decrease of economic growth than positive ones (Mehrara and Oskoui, 2007;Delavari et al., 2008)) A negative shock, on the other hand, typically induces downward adjustments in government expenditures.This adjustment could be very costly.On the one hand, cutting current expenditures is usually unpopular because of its negative social consequences.On the other hand, cutting capital expenditures would disrupt public projects, reducing the productivity of the initial investment and causing high social costs (Anshasy et al., 2006).
If the government spends more on investment when oil prices rise, then, theoretically, it can increase growthassuming that the implementation capacity exists and the investments are indeed productive.Governments will also typically increase consumption, such as wages and salaries, and outright subsidies and transfers, as well as expenditures on health and education.This could have permanent impact, in terms of raising public expectations and ratcheting up current and future expenditure commitments limiting the government's ability to amend fiscal policy when revenues decrease.In the smaller exporting countries in particular, govern-ment expenditure will constitute a large share of total spending and have a profound influence on aggregate demand (Devlin and Levin, 2004).
The positive development in oil prices, which is resulted in higher levels of government expenditures and income per capita, pushes the effective demand upward.Furthermore, the limited capacity of domestic supply and inefficiencies as well as time lags in response to increased demand may push the general consumer prices upward, fueling inflation (Farzanegan and Markwardt, 2009;Frzanegan, 2011).
When oil revenues fall because of negative oil price shocks, the level of imported raw and capital intermediaries, which is mainly financed through oil revenues, will decrease.Thus, domestic production will decrease.This means a shift of the supply curve to the left.Because of deficit spending through borrowing of the government from the central bank (or recently withdrawals from oil stabilization account), which raises the base money and money supply, the demand curve shifts to the right.A combination of these two shifts in demand and supply curves leads to increased prices and to a reduction of the production level in the economy (Farzanegan and Markwardt, 2009;Farzanegan, 2011).
Lower oil rents resulting from an oil price shock cause a temporary shift in the production function, leading to decrease in real output.The decrease in output, ceteris paribus, leads to an excess demand for goods and an increase in the interest rate.This decrease in output and interest rate leads to decrease in the demand for real cash balances, and given a nominal quantity of money, the price level increases.Therefore, we would expect an oil price shock lead to decrease in GDP and increase in price level (Gordon, 1984;Philip and Akintoye, 2006).
In other side, some researchers believe that oil revenues could be positive until a certain level.But after this level the effect turns to be negative.During the oil busts, with the low (or negative) growth rate of oil revenues, the oil-dependent economies suffer from under-capacity with their access to capital and intermediate imports restricted, particularly in the presence of capital market imperfections (Ricardo and Roberto, 2002).So, more oil revenues can be a blessing during the busts or moderate booms.But when oil revenues are excessively high, the real exchange rate becomes highly overvalued.So, too much oil revenues exert a negative effect on growth, turning to be a curse (Mehrara, 2009).
Oil price shocks are a major source of disturbance for the economies of oil-exporting countries given the relative importance of the oil sector in production and exports and uncertainty in the world oil markets.Some of the recent studies will be reviewed.Huanga et al. (2005) used the multivariate threshold autoregressive model (MVTAR) of Tsay (1998) to find the threshold value of an oil price change and its shock in each country.They came to a number of interesting conclusions.First, the most appropriate threshold value appears to be different according to an economy's degree of dependence on imported oil and its attitude towards adopting energy-saving technology.Second, an oil price shock has a limited effect on the economy if the change is below the critical threshold levels for a given economy.Third, if the change is above the threshold levels, it appears that the change in the oil price explains the macroeconomic variables better than the shock caused by the oil price.Finally, an oil price change above the threshold level explains the variation in GDP growth better than the real interest rate.Mehrara and Oskoui (2007) examined the causal relationship between the per capita energy consumption and the per capita GDP in a panel of 11 selected oil exporting countries by using panel unit-root tests and panel cointegration analysis.Data used in the analysis are annual time series during the period 1971-2002 on real GDP and energy used (both per capita) for the 11 oil exporting countries.The results showed a unidirectional strong causality from economic growth to energy consumption for the oil exporting countries.The findings have practical policy implications for decision makers in the area of macroeconomic planning.In most major oil exporting countries, government policies keep domestic prices below free market level, resulting in high levels of domestic energy consumption.The results imply that the energy conservation through reforming energy price policies has no damaging repercussions on economic growth for this group of countries.Anoruo and Mustafa (2007) analyzed the relationship between oil and stock returns for the US using daily data, Johansen Bivariate Cointegration, and error-correction approach.The findings showed long-run linkage between oil and stock returns in the US.The estimated Vectorerror-correction Model (VECM) provided evidence of causality from stock market returns to oil market and not vice versa.Although the Johansen and Juselius estimation technique did not yield evidence of cointegration, the Gregory-Hansen cointegration tests2 provided evidence of both oil and stock markets being cointegrated.The authors argued that this result implied that both markets are integrated and not segmented.Consequently, the authors believed that diversifying in both markets will not create benefits for the investors holding the portfolio because of the integration of the markets, and that risk minimization through portfolio diversification are unattainable by holding assets in oil and stock markets.Rebeca and Marcelo (2008) analyzed the effect of oil price shocks on the output of the main manufacturing industries in six OECD countries.They considered a recursively identified bivariate VAR with real oil price in domestic currency and specific industrial output as variables (entering into the model in that order).The available sample runs from 1975:1 to 1998:12, for all countries but France and Spain, where data start in 1980:1.The pattern of responses to an oil price shock by industrial output is diverse across the four European Monetary Union (EMU) countries under consideration (France, Germany, Italy, and Spain), but broadly similar in the UK and the US.Moreover, evidence on crossindustry heterogeneity of oil shock effects within the EMU countries is also reported.
Rafig et al. ( 2009) examined the impact of oil price volatility on key macroeconomic indicators of Thailand.The impact of the oil price volatility is investigated using the vector auto-regression (VAR) system.This paper used quarterly data from 1993Q1 to 2006Q4 for Thailand.The Granger causality test, impulse response functions, and variance decomposition show that oil price volatility has significant impact on macroeconomic indicators, such as unemployment and investment, over the period from 1993Q1 to 2006Q4.A VAR for the post-crisis period shows that the impact of oil price volatility is transmitted to budget deficit.The floating exchange rate regime introduced after the crisis may be the key contributor to this new channel of impact.Mehrara (2009) examined the issue of the existence of the threshold effects in the relationship between oil revenues and output growth in oil-exporting countries, applying panel regressions.He investigated the nonlinear effects of oil revenue changes on economic activities for 13 oil-exporting countries (Algeria, Colombia, Ecuador, Indonesia, Iran, Kuwait, Libya, Mexico, Nigeria, Qatar, Saudi Arabia, United Arab Emirates and Venezuela) used 5-year-averaged data over the period 1965-2005.The empirical results strongly suggest the existence of a threshold beyond which oil revenues growth exerts a negative effect on output.The results indicated that the threshold of growth rate of oil revenues above which oil revenues significantly slows growth is around 18-19% for oil-exporting countries.In contrast, linear estimation without any allowance for threshold effects would misleadingly imply that an increase in the oil revenues increase the economic growth rate.Failure to account for nonlinearities conceal the resource curse in these countries particularly during extreme oil booms as suggested in previous studies.
Reimer (2009) finds that Iran is affected by the natural resource curse.Iran's economy depends heavily on revenues from oil and gas exports.This dependency poses an obstacle to economic development.Until today, Iran has failed to escape this resource curse and this is caused by an imbalance in the relation between the state and society.The Iranian state fails to provide the necessary institutions that encourage private activity and foreign investment in the economy.The Iranian economy has experienced difficulties to recover from the Iran-Iraq war.In 2006, the GDP per capita still had not reached the level it had in 1976.The main challenges for the Iranian state are the high inflation rate and high unemployment.Iran is affected by the natural resource curse.During the period between 2000 and 2007, Iran experienced an increase in oil and gas export revenues.However, this increase in export revenues was not accompanied by an increase of Iran's share of non-oil exports.Neither did Iran increase the inflow of FDI.Measurements of social capital during the period between 2000 and 2007 show a decrease of political rights and civil liberties and an increase of corruption.The increase in energy export revenues has not resulted in an increase of social capital in Iran.As the income from oil and gas exports increased, so did the perception of corruption among the Iranians.The overall economic growth of Iran has been modest, but the sustainability of this growth is questionable, because of the dependency on the oil and gas sector.Dissou (2010) employed a multi-sector, inter temporal general equilibrium model to provide perspectives on the sectoral, aggregate and dynamic adjustments of a sustained increase in oil prices.It highlights the transmission channels through which the rise in oil prices affects the domestic economy.The simulation results suggest that the shock would have positive aggregate impacts, but would also spur the reallocation of resources and would therefore induce disparities in sectoral adjustments.The suggested contraction in some industries could not however be attributed to a pure Dutch disease phenomenon because of, among other factors, the costpush effect induced by the increase in oil prices.Iwayemi and Fowowe (2011) studied an empirical analysis of the effects of oil price shocks on a developing country oil-exporter-Nigeria.They used quarterly data for Nigeria over the period 1985:Q1 to 2007:Q4.Their findings showed that oil price shocks do not have a major impact on most macroeconomic variables in Nigeria.The results of the Granger-causality tests, impulse response functions, and variance decomposition analysis all showed that different measures of linear and positive oil shocks have not caused output, government expenditure, inflation, and the real exchange rate.The tests support the existence of asymmetric effects of oil price shocks because they found that negative oil shocks significantly cause output and the real exchange rate.Agnani and Iza (2011) focused on Venezuela´s growth experience over the 56-year period from 1950 to 2006, which was characterized by a high economic growth rate during the 1950-1974 expansion periods and a low economic growth rate in the 1974-2006 depression periods which has already been noted by other authors.They showed that the country has been immersed in a 'great depression' since the mid-seventies.They also showed that although Venezuela is oil abundant economy, this growth experience is largely due to the evolution of its non-oil GDP.They perform a growth accounting exercise to quantify the extent to which the growth experience in the non-oil sector is a result of physical capital accumulation, finding that non-oil sector behavior can largely be explained by the evolution of total factor productivity (TFP).Finally, they calculated the correlations between oil rents and physical capital accumulation and TFP in the non-oil sector, finding a high positive correlation during the good performance period, but a negative correlation in the implosion period.

Model estimation
In this section empirical model of asymmetric effects of oil price shocks on production is specified and estimated.In production growth equation, in addition to positive and negative oil price shocks, the effect of other variables, including investment are considered.In this study, growth equation is specified as follows: is gross domestic output (without oil), pos is positive oil price shock, neg is negative oil price shock, X is explanatory variables and  is error term.In addition, asymmetry hypothesis implies: In growth model, various variables are used as control variables in vector X.Some of these variables are: physical investment, human capital, free trade, inflation rate, population, government expenditures, geographical variables, foreign direct investment, exchange rates premium, abundant natural resources, institutions and the quality of macroeconomic policy.In this study, due to the limited sample size, availability of data and diagnostic test, different combinations of variables, such as government expenditures growth, (Δ ln G), Liquidity growth, (Δln M2), inflation rate, (Δ ln P), real money supply growth (Δ lnM2/P), the percentage changes in real exchange rate, (Δ ln EX), investment to GDP ratio (inv/y) or investment growth (Δ ln inv), as control variables in vector X are used.In fact, government expenditures, money balance and inflation variables as the demand side factors and investment ratio as the supply side factor affect the production.
One of the important and considerable factors in this model is estimation method of positive and negative oil price shocks.The methodology of estimation of positive and negative oil price shocks is as follows.

Positive and negative oil price shock
In empirical studies, any unanticipated change is considered as the shock.Researchers used different techniques for differentiation between positive and negative shocks.For example, Mishkin (1982), Cover (1992), Karras (1996) considered the residual of the money supply growth equation (M2) as monetary shocks.In fact, in these studies money growth is divided into anticipated and unanticipated ones, and the residual from the estimated equation of money growth is used as unanticipated monetary shock.
To analyze the asymmetric effects of exchange rate shocks on the relevant macroeconomic variables, Kandil (2000) decomposed the exchange rate shock to its positive and negative components, as follows: Where, is the exchange rate shock and and are the negative and positive components of the shock or, to express it differently, unexpected depreciation and appreciation of the exchange rate.
The scaled and net specifications were developed by Lee et al. (1995), respectively, to account for the fact that oil price increases after a long period of price stability have more dramatic macroeconomic consequences than those that are merely corrections to greater oil price decreases during the previous quarter.In order to put this idea in practice, these authors use some transformation of the oil price variable.Lee et al. (1995) proposed the following AR(4)-GARCH(1,1) representation of oil prices:

Where
stands for scaled oil price increases, while for scaled oil price decreases.The scaled model builds on the asymmetric model, while it also employs a transformation of the oil price that standardizes the estimated residuals of the autoregressive model by its time-varying (conditional) variability.This transformation seems very plausible in light of the pattern of oil price changes over time, with most changes being rather small and being punctuated by occasional sizeable shocks.
Another method of decomposing positive and nega-tive shocks is using univariate filtering of Hodrick-Prescott (1997).This smoothing filtering is widely used in real business cycle theory to separate the cyclical component of a time series from raw data.Let Xt denote the logarithms of a time series variable.The series Xt is made up of a trend component, denoted  and a cyclical component given an adequately chosen, positive value of  , there is a trend component that will minimize The first term of the equation is the sum of the squared deviations which penalizes the cyclical component.The second term is a multiple  of the sum of the squares of the trend component's second differences.This second term penalizes variations in the growth rate of the trend component.The larger the value of  , the higher is the penalty.Hodrick and Prescott advise that, for annual data, a value of  = 100 are reasonable.In this article we use Hodrick Prescott technique.
Figure1 shows Hodrick Prescott (HP) filtering: Next we define a variable called shock as follows: Shock= lnoil-HP Positive and negative shocks are separated as follows: Positive shock=maximum (shock, 0) and negative shock=minimum (shock, 0).Hodrick-Prescott Filter (lambda=100) According to ADF and PP tests in Table 1, it can be seen that all variables except the investment to GDP ratio, INV/GDP, are integrated of order one so that when first differenced, all would be stationary.

Cointegration test
As the level variables are non-stationary, the cointegration among the levels of the variables should be tested.It is expected that the real oil revenue, investment, and GDP have an equilibrium relationship.If there is long run relationship between these variables, the residuals from the cointegrating relationship will be considered as non-oil GDP imbalance affecting GDP symmetrically or asymmetrically.Therefore, the cointegration among these variables is tested by using the Johansson methodologies.The test results are presented in Table 2.As it can be seen in the table, Johansson test confirms one long run equilibrium relationship between these three variables.According to Granger representation theorem, a long run equilibrium relation-ship implies error correction mechanisms.The error correction mechanism ensures the long run relationship.Thus at least one variable in the relation-ship should react to nonoil GDP imbalances or the residuals of long run relationship, namely ECM.In the next section we examine the importance of non-oil GDP imbalances along with other variables on the production growth.Also, these imbalances may affect the production linearly (symmetric) or nonlinearly (asymmetric).

Estimating the short run non-oil GDP and asymmetric test
In this section, the effects of positive and negative oil shocks as well as the supply and demand side factors on the production growth in Iran economy will be studied.For this purpose, we estimate various specifi-cations according to Table 3a.The in colu-mns one to eight are based on linear or symmetrical specifications.other words, in these equations it is assumed that the effects of positive and negative oil shocks on real production are symmetric so that the relationship is linear.In all linear specifications, according to , explanatory variables explain 63 to 83 percent of real non-oil GDP changes.The coefficients for the investment growth, loginv, in all the specifications are significant and of the expected sign (positive).It shows that, the investment is positive and significant in the real non-oil GDP growth equations with the size of coefficient changing between 0.19 to 0.21.Using the investment to GDP ratio instead of the, loginv, renders similar results.The investment to output ratio (INV/GDP) also raises the economic growth rate significantly by 0.45, but the effect will decrease fairly in the next period.Real oil revenue in symmetry specification increases the GDP by coefficient of 0.03 to 0.06.Thus the results show the positive relation between real oil revenue and investment with GDP.The government expenditure is positive and significant in the real non-oil GDP growth equations with the size of coefficient of 0.13; the exchange rate is positive and significant in the real non-oil GDP growth equations with the size of coefficient changing between 0.05 to 0.06; the Liquidity is positive and significant in the real non-oil GDP growth equations with the size of coefficient changing between 0.19 to 0.25; the inflation is negative and significant in the real non-oil GDP growth equations with the size of coefficient changing between -0.12 to -0.17; the real money supply is positive and significant in the real non-oil GDP growth equations with the size of coefficient changing between 0.15 to 0.17.Error correction coefficient ) 1 ( ecm reflects the adjustment speed of output with respect to the oil revenue disequilibrium.Considering the size of coefficient of error correction term (estimated between0.03to 0.08) it can be concluded that non-oil GDP responds significantly to its disequilibrium ( )

( ecm
).Among the linear specifications, the third one outperforms the others based on the R2, Akaike (AIC) and Schwartz (SIC) information criteria.Diagnostic test results are presented at the bottom of the The first to seventh specifications reflect the symmetric effects of positive and negative oil shocks on production.But if oil effects are asymmetric, the results of these models may be misleading.As it was explained in previous section, to examine and test the asymmetric effects of oil shocks on real production, oil revenue changes are divided into positive and negative ones and added as two explanatory variables to the growth model using Hodrick Prescott technique.Specifications 8 to 20 in Table 3b are estimated decomposition of oil shocks to positive (pos) and negative (neg) ones.
As it can be seen by adding positive and negative shocks to the growth equation, the coefficient of determination significantly increases (from 65 to 88 percent).In all cases, the negative oil shocks are much more effective than the positive oil shocks contemporaneously according to the size and statistical significance.
Although in most equations positive oil shocks have positive and significant effect on GDP, in the next period (based on the coefficient ) 1 ( pos ) they have a negative effect on GDP with the same amount.In the other words, the positive effect will be neutralized in the next time.Negative oil shocks 1 have negative and significant effect on GDP in most equations (-0.04 to -0.07).The lag of negative oil shocks is not significant (based on the coefficient )

( neg
) in any of the equation.The estimation results from the above mentioned specifications indicate that long-run positive (ecm1) and negative (ecm2) imbalances also have asymmetric effects on economic growth.The size of coefficient of (ecm1), ranging from 0.02 to 0.03 is much less than the coefficient of (ecm2) which is estimated between 0.07 to 0.08.In addition, coefficient of (ecm1) is not significant in any equation, while the (ecm2) has important effects on (decreasing) economic growth.
Among asymmetric specifications, equation 17 enjoys the best base on , Akaike (AIC) and Schwartz (SIC) criteria.In most of the equations, the coefficients of the variables of the investment, are significant and of correct sign.
The estimated growth equation 17 passes through all diagnostic tests (Heteroscedasticity, Ramsey's RESET test, autocorrelation and normality).In addition, the preferred specification is able to explain 88% of changes in GDP growth.Thus 12% of production changes are 1 Negative oil shocks have negative effect on GDP, the positive coefficient of neg variable in table (3) because of the way the negative shocks is defined is in 4.2.

Conclusion
This paper examines the asymmetric effects of oil price shock on Iran economic growth as an oil exporting country for the period of 1980-2010 using Johansen cointegration test.The results from short run estimations indicate that oil shocks have a significant effect on economic growth.But the effects of negative shocks are much stronger than the positive shocks.In other words, the relationship between two variables is asymmetric.It means that production growth responds stronger to the negative shocks than to positive shocks.
In addition, the effects of oil revenue on economic growth have opposite signs in long run and short run as being negative and positive respectively.Policy-makers must deploy institutional mechanisms to manage oil booms and busts through expenditure restraint, self-insurance, and diversification of the real sector.To achieve  sustainable growth in the future, they must take policy measures that substantially enlarge and diversify their economic base.This should go in tandem with measures needed to enhance their capacity to withstand adverse external shocks and lessen their exposure to the volatility.Moreover, to insulate the economy from oil revenue volatility requires de-linking fiscal expenditures from current revenue.So, an oil revenue fund is one such institutional mechanism for managing the oil revenues.
An oil fund may serve to stabilize the flow of revenue by insulating the Government, and the economy, from revenue shocks that arise from the un-predictable nature of resource extraction and petroleum prices.Instead, this volatility and uncertainty is transferred to the Petroleum Fund, and the State uses a mechanism to limit expenditure.In this way a state can reduce the risk of 'Dutch disease' or volatility in the exchange rate, because revenue is not spent as quickly (or slowly) as it comes.Such funds are often referred to as 'stabilization funds'.Such funds operate in response to external factors, so they could quickly accumulate revenue, or are rapidly exhausted.Thus, a fund based on contingencies provides less stability, because a state cannot be certain of the annual spending amount that will be available to it, and must expect fluctuations in available revenue.If, instead, a state uses a rule for fund deposits, and withdrawals, which is not affected by externalities, the flow of petroleum revenue can be more stable.
Another way that policy makers could decrease the degree of the asymmetry would be to lower borrowing constraints so that agents could better smooth consumption and so not cut spending as drastically following a negative price shock.Perhaps developing deeper capital markets is one solution.
Time series data required to this research include non-oil GDP(Y), real oil revenue (OILREV), money supply(M2), aggregate price level(P),exchange rate(EX), government expenditures(G) and fixed capital formation or investment to GDP ratio (INV/GDP).The sources for data are balance sheets of the Central Bank of Iran during the period 1960-2005.The cointegeration analysis is subject to the inte-gration order of time series.The integration orders of variables are examined by Augmented Dickey -Fuller (ADF) and phillips-Perron (PP) unit root tests.

NORM
Notes: t-ratios in parentheses and ***, **and * respectively show the significance in 1, 5 and 10% levels.yetattributable to factors that are not included in the model.Due to severe structural changes in the sample period (especially Iran-Iraq War and Islamic Revolution) stability of structural coefficients based on the plot of cumulative sum of recursive residuals (CUSUM) and plot of cumulative sum of squares of recursive residuals (CUSUMSQ) have been used.The plot of CUSUM and CUSUMSQ statistics together with the 5% critical lines clearly indicates stability in equation and residual variance during the sample period (Figures2 and 3).

Figure 2 .
Figure 2. CUSUM test for parameters stability in the growth equation.

Figure 3 .
Figure 3. CUSUMSQ test for parameters stability in the growth equation.

Table 1 .
PP and ADF test statistic variables in level and 1st difference.

Table 2 .
Maximal eigenvalue and trace test for cointegration vectors.
Notes: t-ratios in parentheses.
Table 3 for each specification. 2  AR (2) stand for the Lagrange multiplier test statistic for Rezazadehkarsalari al. autocorrelation in error terms ( with two lags), RESET is Ramsey's RESET test statistic for functional form misspecification based on the squares of fitted values, NORM is test statistic of normality of residuals based on the skewness and kurtosis and HET is Heteroscedasticity test statistic.As it can be seen, the obtained results are generally satisfactory.

Table 3a .
Estimation of model with different specifications.

Table 3B .
Estimation of model with different specification.NORM Notes: t-ratios in parentheses and ***, **and * respectively show the significance in 1, 5 and 10% levels.