Productivity, Crude Oil Supply Shocks and the Economy of Iran in a Dynamic Stochastic General Equilibrium Framework

Abstract

This paper investigates the responses of key macroeconomic variables—including output, consumption, investment, capital accumulation, and employment—to productivity and oil market shocks in Iran. Using quarterly data from 1975 to 2024, we estimate an RBC DSGE model and further calibrate a variant incorporating oil supply shocks. The results show that positive productivity and oil shocks generate immediate expansions in output, consumption, investment, labor hours, and wages, reflecting strong short-term multipliers and forward-looking behavior by households and firms. However, these gains are temporary, with impulse responses following a hump-shaped path and gradually declining as shocks dissipate, capital depreciation sets in, and policy or market frictions emerge. This underscores the transient nature of both productivity and oil windfalls and the need for policies that mitigate short-run volatility while fostering structural reforms and diversification. For an oil-dependent economy such as Iran, the findings highlight that while shocks can stimulate activity in the short term, sustainable long-run growth requires institutional mechanisms to channel temporary gains into lasting development.

1. Introduction

The Iranian economy has been reeling from high inflation for a few decades. As an economy that heavily depends on crude oil and the global market for crude oil, oil price fluctuations play a dual role in Iran’s inflation. On the cost side, higher oil prices may nominally boost revenues, but under sanctions this often fails to translate into broader economic gains. Instead, costs for energy, transportation, and production increase across the economy.

Geopolitical turmoil, such as tensions around the Strait of Hormuz, raises global crude prices—leaving Iran caught between higher import costs and volatile revenue streams. When Iran’s infrastructure cannot absorb such shocks, it leads to supply constraints and upward pressure on prices. Rising production costs—particularly energy—pass straight through to consumer prices, as expected based on the cost-push inflation.

Iran’s economy has been shaped by recurring geopolitical tensions, international sanctions, and a heavy dependence on oil revenues. These factors have contributed to fiscal imbalances, exchange-rate pressures, and periods of elevated inflation. More importantly for the purposes of this study, sanctions and external shocks have affected the economy through two key channels emphasized in the DSGE framework: productivity and oil-sector performance. Sanctions have constrained access to foreign technology, capital goods, and international financial markets, thereby reducing productive efficiency and limiting growth in total factor productivity. At the same time, restrictions on oil exports and fluctuations in global energy markets have generated substantial volatility in oil production and oil revenues, affecting investment, consumption, employment, and overall economic activity.

The depreciation of the rial and the resulting increases in import costs have further complicated economic adjustment by raising production costs and reducing the availability of imported intermediate inputs. These developments can adversely affect productivity and amplify the macroeconomic consequences of oil-sector disturbances. Because oil revenues remain an important source of national income, fluctuations in oil production and export earnings continue to play a central role in shaping business-cycle dynamics in Iran. Consequently, understanding how productivity shocks and oil supply shocks propagate through the economy is essential for explaining the fluctuations in output, consumption, investment, labor utilization, and capital accumulation examined in this study.

In this paper, we investigate how productivity shocks and crude oil market shocks—specifically oil supply disturbances—propagate through the Iranian economy within a Dynamic Stochastic General Equilibrium (DSGE) framework. Given Iran’s structural dependence on crude oil revenues, recurrent sanctions, exchange rate pressures, and chronic fiscal imbalances, understanding the transmission mechanisms of oil-related shocks is essential for evaluating macroeconomic stability. Specifically, we examine three central questions. First, how do productivity shocks affect output, consumption, investment, capital accumulation, labor supply, wages, and oil production in an oil-dependent economy? Second, how do positive oil supply shocks transmit to key macroeconomic aggregates, and do these shocks generate temporary expansions or persistent deviations from steady state? Third, to what extent do oil supply shocks amplify or dampen the effects of domestic productivity disturbances in a resource-dependent economy with limited diversification?

The Real Business Cycle–Dynamic Stochastic General Equilibrium (RBC–DSGE) framework is grounded in neoclassical general equilibrium theory and intertemporal optimization. At its core, the model assumes rational, forward-looking households and profit-maximizing firms operating in competitive markets, where prices and wages adjust to equate supply and demand. Economic fluctuations arise as efficient responses to exogenous real shocks—most commonly productivity disturbances—rather than from nominal rigidities or persistent market imperfections. The theoretical foundation draws from the Walrasian tradition of general equilibrium, the intertemporal consumption–savings problem, and the optimal accumulation of capital under uncertainty. Within this structure, aggregate dynamics are derived from microeconomic behavior, ensuring internal consistency between household decisions, firm production choices, and equilibrium outcomes.

These theoretical underpinnings align closely with key structural characteristics of the Iranian economy. Iran operates under persistent geopolitical uncertainty, recurrent sanctions, volatile oil revenues, and frequent external shocks. In such an environment, long-term nominal contracts are difficult to sustain, as inflation volatility and exchange rate fluctuations undermine their credibility. The practical difficulty of enforcing extended wage agreements and price commitments implies that wages and prices tend to adjust more flexibly than in highly stable, contract-intensive economies. This relative flexibility supports the RBC assumption that markets clear through price adjustments rather than through prolonged quantity rationing or nominal stickiness.

Moreover, despite a significant public sector presence, the production of goods and services in Iran remains heavily dependent on private firms responding to market incentives. Investment, labor supply, and production decisions are shaped by expected returns, input costs, and productivity conditions. These features are consistent with a framework in which representative agents optimize intertemporally and firms respond to real shocks by adjusting capital and labor inputs. In this context, macroeconomic fluctuations—whether triggered by oil shocks, sanctions, or productivity disturbances—can be interpreted as equilibrium responses to changing constraints rather than as failures of coordination.

Taken together, the theoretical structure of the RBC–DSGE model provides a coherent lens through which to analyze the Iranian economy. Its emphasis on real shocks, forward-looking behavior, and flexible adjustment mechanisms corresponds to an economic environment characterized by external volatility, contract uncertainty, and market-driven production decisions.

By embedding crude oil explicitly as an input in the production structure and modeling oil production shocks as exogenous stochastic processes, the DSGE framework allows us to identify the dynamic interactions between the real sector and the oil sector. The model’s impulse response analysis enables us to assess whether oil-driven income effects generate sustained growth or merely short-lived expansions followed by reversion to trend. In doing so, the paper clarifies the quantitative role of oil in shaping macroeconomic volatility in Iran and evaluates whether oil supply shocks act as stabilizing buffers or as sources of cyclical amplification.

In the macroeconomic literature, Dynamic Stochastic General Equilibrium (DSGE) models, building upon the Real Business Cycle (RBC) framework, have become a prevalent tool for analyzing the effects of exogenous shocks on an economy. These micro-founded models are particularly useful for understanding the transmission mechanisms of commodity price fluctuations and their impact on key macroeconomic variables, often employing impulse response function analysis following estimation or calibration.

The consensus among the macroeconomists is that DSGE models enjoy some advantages compared to partial equilibrium models.

To grasp the complexities, advantages, and disadvantages of DSGE models, we encourage readers to see Korinek (2018) and Gorodnichenko and Ng (2010). In the next section, we offer a brief review of the relevant literature. Section 3 briefly explains the RBC DSGE model. Data are described in Section 4. Empirical results are the topic of Section 5. A summary and conclusions are the subject of Section 6.

2. Literature Review

Building on the foundational work of Christiano and Eichenbaum (1992), Christiano et al. (2005), Smets and Wouters (2003, 2007), and Galí (1999), among others a cluster of studies applies DSGE models to the analysis of commodities, energy markets, and oil price shocks. L. Medina (2010) constructed a DSGE model for Chile that incorporated oil into consumption and production, illustrating how oil shocks transmit to employment, GDP, and monetary policy. Dagher et al. (2010) developed a DSGE framework tailored for Ghana to examine the macroeconomic consequences of oil discoveries, underscoring the need for stabilization funds to mitigate Dutch Disease effects. Balke and Brown (2018) designed a U.S. model that explicitly integrates oil into consumption, intermediate production, transportation, and domestic supply, finding that global oil supply shocks are contractionary but have smaller effects on GDP than earlier models suggested. Aminu (2019) examined the United Kingdom, showing how volatile energy prices shape inflation, output, and monetary policy trade-offs. Zhang et al. (2022) analyzed China’s economy, distinguishing between supply- and demand-driven oil shocks and their effects on output, investment, and the new energy sector. Similar approaches appear in Rasaki and Chukwu (2024), who calibrated a DSGE model for South Africa to study terms-of-trade shocks, while Fornero and Kirchner (2018) highlighted how imperfect information about the persistence of commodity shocks influences investment booms and current account balances. More recent contributions such as Ávila-Montealegre et al. (2025) have advanced Bayesian methods for incorporating commodity shocks, while Lubik and Schorfheide (2005) demonstrated how Bayesian estimation can improve identification of DSGE models with labor and output data. Together, this body of research demonstrates the adaptability of DSGE frameworks for analyzing the macroeconomic implications of energy volatility and commodity dependence across diverse national contexts.

Beyond their use in understanding commodity shocks, DSGE models have also become central to policy analysis and forecasting. Poutineau and Vermandel (2015) showed how cross-border financial transactions amplify business cycles in the eurozone, while Edge et al. (2007, 2008a, 2008b, 2009a, 2009b) introduced the FRB/EDO model at the U.S. Federal Reserve to study investment shocks, IS-curve fluctuations, and long-run growth. Burriel et al. (2010) extended such models to fiscal and monetary policy applications, while Bhattarai and Trzeciakiewicz (2017) explored interactions between fiscal and monetary regimes. Several authors, including Lindé (2018) and Blanchard (2018), debated the strengths and weaknesses of DSGE models as central banking tools, highlighting their ability to provide structured policy simulations despite mixed forecasting records. Contributions by Sharma and Behera (2022) applied DSGE approaches to monetary policy evaluation, while Smets and Wouters (2004), Schorfheide et al. (2010), Edge and Gürkaynak (2011), Edge et al. (2013), Alpanda et al. (2011), Fernández-de-Córdoba and Torres (2011), Del Negro and Schorfheide (2013), Wickens (2014), Wolters (2015), Kolasa and Rubaszek (2015), Balcilar et al. (2015), and Ahmad and Haider (2019) further illustrate the breadth of applications. Taken together, these studies emphasize how DSGE models have become indispensable for central banks and policymakers, not only as forecasting tools but also as structured frameworks for evaluating the consequences of policy choices. Other notable applications of DSGE models across a variety of research areas include the studies of Malakhovskaya and Minabutdinov (2014), Cai et al. (2019), Millard (2011), and Morris (2016).

In summary, the literature demonstrates a clear evolution: from foundational RBC models, to enriched New Keynesian DSGEs, to applications in resource-dependent economies and central bank policymaking. By capturing dynamic interlinkages, incorporating frictions and uncertainty, and allowing for structural interpretation, DSGE models have established themselves as vital instruments for analyzing oil shocks, macroeconomic fluctuations, and policy interventions.

The analysis of impulse response functions (IRFs) in the DSGE literature reveals several common patterns as well as distinctive findings that have shaped the evolution of business cycle research. Early RBC models, such as those discussed by Christiano and Eichenbaum (1992) and Cogley and Nason (1995), showed that while technology shocks could generate fluctuations in output and wages, the models often produced excessively volatile real wages and insufficiently volatile hours worked. These studies highlighted the inability of standard RBC frameworks to capture the persistence of output and labor dynamics observed in actual data, with output responses appearing too sharp and short-lived rather than the gradual, hump-shaped patterns seen empirically.

When extended to resource- and energy-dependent economies, DSGE models confirmed the significant role of commodity shocks. Dagher et al. (2010) showed that oil windfalls in Ghana caused real exchange rate appreciation and Dutch Disease effects, with tradable sectors contracting as non-tradable output expanded. Balke and Brown (2018) found that global oil supply shocks in the U.S. reduced GDP, though less strongly than in older studies, with inflationary pressures emerging as a key consequence. Aminu (2019) highlighted that in the UK, the persistence of energy price shocks mattered greatly: temporary shocks caused only transitory GDP declines, while permanent shocks produced deeper, long-lasting contractions. Zhang et al. (2022) distinguished between demand- and supply-driven oil shocks in China, showing how each had differing effects on investment and new energy development. Similar themes arose in Rasaki and Chukwu (2024) for South Africa and Fornero and Kirchner (2018) for other commodity exporters, both stressing the importance of terms-of-trade shocks and the role of expectations in driving investment and external balance dynamics.

Across these contributions, a set of robust findings emerges. Technology shocks generally increase output, consumption, investment, and real wages, but often with weak or negative short-run employment responses (Christiano & Eichenbaum, 1992; Smets & Wouters, 2003, 2007). Monetary policy shocks display persistent real effects due to frictions, with contractionary policy reducing output and investment while curbing inflation (Christiano et al., 2005; Smets & Wouters, 2007). Commodity price and oil shocks tend to depress output and investment while raising inflation, though their severity depends on the persistence of shocks and the structure of the economy (Dagher et al., 2010; Balke & Brown, 2018; Aminu, 2019; Zhang et al., 2022). Importantly, institutional factors such as fiscal rules, stabilization funds, and credible monetary policy responses strongly influence the extent to which shocks propagate through the economy (Fornero & Kirchner, 2018; Rasaki & Chukwu, 2024). These consistent themes, combined with the standout puzzles like muted labor responses to productivity shocks, demonstrate both the strengths and the continuing challenges of DSGE models in capturing the complex dynamics revealed through impulse response analysis.

The DSGE literature on resource-dependent economies has also reached several broad areas of agreement. First, oil and commodity shocks are important drivers of macroeconomic fluctuations in countries that depend heavily on natural-resource revenues. Across a variety of settings, positive oil supply or commodity-price shocks generally increase output, consumption, investment, and wages in the short run, while negative shocks produce contractions in economic activity. Second, these effects are typically temporary, with the persistence of responses depending on the nature of the shock and the institutional characteristics of the economy. Third, fiscal rules, stabilization funds, monetary policy responses, and expectations play an important role in determining how commodity shocks are transmitted and amplified.

Despite these common findings, important differences remain in the literature. Studies of advanced commodity exporters such as Norway and Chile generally find that strong institutions and stabilization mechanisms reduce the volatility associated with commodity shocks. In contrast, studies of emerging and developing commodity exporters often report larger and more persistent responses due to greater dependence on resource revenues and weaker shock-absorption mechanisms. Existing research also differs regarding the relative importance of productivity shocks versus commodity shocks, the persistence of oil-related disturbances, and the extent to which resource booms generate lasting gains in output and investment.

Further limitation of the literature is that most DSGE studies focus on countries such as Chile, Ghana, Norway, South Africa, China, and the United States. Although these economies provide valuable insights, their institutional structures differ substantially from those of Iran. Iran combines a high degree of oil dependence with recurring international sanctions, exchange-rate pressures, fiscal imbalances, and restricted access to international capital markets. These features may alter both the transmission and persistence of oil and productivity shocks in ways that cannot be inferred directly from studies of other commodity-exporting economies.

The present study addresses this gap by developing and estimating a DSGE model specifically tailored to the Iranian economy. Unlike most previous studies, the model explicitly incorporates crude oil as a productive input while simultaneously distinguishing between productivity shocks and oil supply shocks. This framework makes it possible to compare the relative importance of these two sources of fluctuations and to evaluate whether oil-driven expansions generate sustained growth or only temporary deviations from the long-run equilibrium path. Consequently, the contribution of this paper is not merely the application of an existing DSGE framework to a new country, but the provision of structural evidence on how productivity and oil-sector disturbances jointly shape macroeconomic dynamics in one of the world’s most oil-dependent and sanction-constrained economies.

3. The Real Business Cycle DSGE Model Including the Crude Oil Input

Dynamic Stochastic General Equilibrium (DSGE) models provide a coherent framework for analyzing how exogenous shocks propagate through an economy via optimizing households and firms operating in general equilibrium. It is based on the seminal work of Kydland and Prescott (1982), who demonstrated that business cycles could be interpreted as optimal responses of rational agents to real shocks, particularly technology shocks. King et al. (1988) extended this work by integrating long-run growth theory with cyclical dynamics.

The Real Business Cycle–Dynamic Stochastic General Equilibrium (RBC–DSGE) framework employed in this study is grounded in neoclassical economic theory, which interprets macroeconomic fluctuations as rational and efficient responses of optimizing agents to exogenous real shocks. This perspective is particularly relevant in the context of the Iranian economy, which has repeatedly faced severe external disturbances—including oil price volatility, international sanctions, exchange rate pressures, and geopolitical tensions—and has adjusted through measurable changes in output, investment, labor supply, and consumption patterns. The RBC–DSGE structure provides a coherent analytical framework for interpreting these adjustments as equilibrium responses rather than as systematic market failures.

The central assumption of the model is that households and firms form rational expectations. Economic agents are assumed to utilize all available information and understand the structural relationships governing the economy when forming expectations about future variables. In an environment such as Iran’s—where agents routinely confront inflationary pressures, exchange rate depreciation, and policy uncertainty—forward-looking behavior is especially relevant.

The model further adopts the representative-agent framework, in which a single household maximizes the present value of expected lifetime utility. Utility depends positively on consumption and negatively on labor effort, reflecting the standard trade-off between consumption and leisure.

Markets are assumed to be perfectly competitive, with fully flexible prices and wages that adjust to clear goods and factor markets in each period.

Although the assumptions of perfectly competitive markets and fully flexible prices and wages are standard in the RBC literature, they should be interpreted as analytical benchmarks rather than literal descriptions of the Iranian economy. Iran’s economic environment includes substantial government involvement in key sectors, energy subsidies, administered prices, exchange-rate controls, state-owned enterprises, and periodic sanctions that may generate distortions in both product and factor markets. These institutional features can slow adjustment processes and create deviations from the frictionless equilibrium characterized by the model. Nevertheless, the benchmark RBC-DSGE framework remains useful because it provides a transparent setting for identifying the fundamental transmission mechanisms of productivity and oil-sector shocks. The results should therefore be interpreted as illustrating the economy’s underlying equilibrium responses to real shocks, abstracting from many short-run frictions and policy distortions that may influence the magnitude and timing of observed outcomes.

Fluctuations in the model are driven by exogenous real shocks, particularly changes in total factor productivity and oil-related disturbances. In the Iranian context, productivity shocks may arise from technological changes, sanctions-induced constraints, or shifts in production capacity, while oil supply shocks reflect variations in global markets and domestic extraction conditions. These shocks shift the production function and alter relative prices, triggering endogenous adjustments in output, consumption, investment, and labor supply.

The algebraic derivations and manipulations underlying DSGE model equations are well established in the literature and can be readily consulted elsewhere (see Adrangi & D’Amico, 2023). To maintain brevity, the detailed steps of these derivations for each block of the model are omitted here.

In this baseline setup, general equilibrium outcomes are determined by the optimization behavior of households and firms. The model assumes zero population growth, flexible wages, and a perfectly competitive labor market. The utility function is taken to be quasiconcave, with consumption and leisure each yielding strictly positive but diminishing marginal utility.

Given these assumptions, the household’s objective is expressed as the present value of expected utility:

$$\underset{C_t,L_t}{Max}{E}_t{\displaystyle \sum _{t=0}^{\infty }{\beta }^t(C_t^{\sigma },H_t^{\gamma }}), \mathsf{\beta }>0,$$

(1)

In this framework, E denotes the expectations operator, β is the intertemporal discount factor, σ measures the degree of risk aversion, and γ captures the marginal disutility associated with supplying labor.

The household’s budget constraint is defined by two main income sources: labor earnings at the competitive wage rate and the return on accumulated capital. At each point in time t, income is either consumed or saved and invested, giving rise to the following intertemporal condition:

C_t + S_t = W_tH_t + R_tK_t

(2)

Here, W_t represents the wage rate, R_t is the return to capital K_t, and part of this return reflects dividends distributed to households as compensation for their invested capital. Households are assumed to supply only labor services earn wages from work performed, but they do not hold ownership claims over crude oil resources. Savings are assumed to be channeled into firm investment, leading to capital accumulation over time. The evolution of the capital stock is therefore determined by household savings (equal to investment) net of depreciation:

K_t+1 = I_t + (1 − δ)*K_t,

(3)

where δ is the capital depreciation rate.

Solving the household’s utility maximization problem under the budget constraint yields the first-order optimality conditions, expressed in the form of Euler Equations (4) and (5).

$${\displaystyle \frac{C_{t+1}}{C_t}}=\beta (R_{t+1}+1-\delta ).$$

(4)

The Supply of labor hours is given by

$$H_t={\displaystyle \frac{W_t-\gamma C_t}{W_t}}.$$

(5)

In this model, firms are assumed to operate under perfect competition and employ a Cobb–Douglas production technology that is homogeneous of degree one, as specified in Equation (6).

$$Y_t=A_t{K}_t^{\alpha }{H}_t^{\phi }{O}_t^{\varphi },$$

(6)

where the oil input is assumed to be exogenous as

O_t = z_t m_t and z represents oil supply shocks, modeled as a first-order autoregressive process of the form log (z_t+1) = log z_t + ε_t.

In Equation (6), A denotes total factor productivity, Y_t is output at time t, K_t represents the capital stock, H_t indicates hours of labor supplied, and O stands for the quantity of oil used as an input. The parameters  α, $\phi$, and ϕ capture the output elasticities with respect to capital, labor, and crude oil, respectively. The production function is assumed to be strictly quasiconcave, ensuring positive marginal products for each input.

Within this competitive environment, representative firms maximize profits by optimally choosing their levels of labor and capital. With the price level normalized to unity, the profit function takes the form specified in Equation (7).

$$\underset{K,H}{Max}{\mathsf{\Pi }}_t=A_t{K}_t^{\alpha }{H}_t^{\phi }{O}_t^{\varphi }-W_t{H}_t-R_t{K}_t-P{O}_t{O}_t,$$

(7)

where PO is the price of a unit of crude oil input at time t.

Equations (8)–(10) are the input demand functions for K_t, H_t, and O_t which are derived by taking the partial derivatives of Equation (7) with respect to K, H, and O, substituting for the production function in each partial derivative equation, and setting them equal to zero.

$$R_t=\alpha {\displaystyle \frac{Y_t}{K_t}},$$

(8)

$$W_t=\phi {\displaystyle \frac{Y_t}{H_t}}.$$

(9)

$$P{O}_t=\varphi {\displaystyle \frac{Y_t}{O_t}}.$$

(10)

• Equilibrium Condition

The equilibrium of the model is characterized by a set of equations that capture the interaction between households and firms. At equilibrium, aggregate output must equal aggregate demand, implying

Y_t = C_t + I_t

where investment I_t corresponds to household savings in period t. The complete structure of the economy is therefore summarized in the following system of equations.

• Household block

$${\displaystyle \frac{C_{t+1}}{C_t}}=\beta (R_{t+1}+1-\delta )$$

(11)

$$H_t={\displaystyle \frac{W_t-\gamma C_t}{W_t}}$$

(12)

• Firm block

K_t+1 = I_t + (1 − δ)*K_t

(13)

$$Y_t=A_t{K}_t^{\alpha }{H}_t^{\phi }{O}_t^{\varphi }$$

(14)

$$W_t=(1-\alpha -\varphi ){\displaystyle \frac{Y_t}{H_t}}$$

(15)

In equilibrium, the model imposes the standard condition that total output (GDP) must be allocated either to consumption or to savings and investment, expressed as follows.

Y_t = C_t + I_t

(16)

To the system of equations, we incorporate total factor productivity (TFP) shocks, which in the RBC framework are typically attributed to technological progress. Consistent with standard practice in the literature, these shocks are modeled as a first-order autoregressive (AR(1)) process.

ln(A_t+1) = ρ ln(A_t) + e_t+1.

The system of equations in the model is inherently nonlinear. A standard approach to handling this is to apply log-linearization techniques, such as those outlined in Uhlig’s method. In this study, however, we proceed by estimating the model directly in terms of the logarithms of the variables.

The steady state of a D GE model builds upon the foundations of Walrasian general equilibrium theory. Walras in 1874 first established the principle that a unique equilibrium could exist in a competitive system. Debreu (1952) provided formal proof of equilibrium in a system of interacting agents, while Arrow and Debreu (1954) extended this result to economies that incorporate both production and consumption activities. Later, Wald (1951) offered a rigorous demonstration of how markets converge to a stable equilibrium, and Arrow and Hurwicz (1958) along with Arrow et al. (1959) further advanced the concept of steady-state equilibrium drawing from earlier work.

In this framework, once a competitive general equilibrium is established, steady-state values of model variables can be defined. Walras’s Law implies that the aggregate excess demand across markets equals zero. By normalizing the price level to one and applying the homogeneity of degree zero property of the excess demand function, algebraic manipulation yields the steady-state levels of variables, denoted by the subscript (s). At steady state, variables become stationary, meaning their expected values remain constant over time. Formally stated, E_tx_t+1 = x_t = x_t−1 = x_s.

In the conventional RBC framework, total factor productivity (A) is treated as an exogenous variable, taking the value of unity in the steady state. The steady-state representation of the model is captured by Equations (17)–(23). This system determines the long-run equilibrium values of the seven endogenous variables: output Y_t, consumption C_t, investment I_t, capital stock K_t, wage rate W_t, hours worked H_t, and the return on capital R_t. The specific steady-state solutions for these variables are reported in the empirical section.

$$C_s=\beta (1-\delta +R_s)$$

(17)

$$K_s=I_s+\left(1-\delta \right)\ast K_s$$

(18)

$$Y_t=A_t{K}_t^{\alpha }{H}_t^{\phi }{O}_t^{\varphi }$$

(19)

$$I_s=\delta \ast K_s$$

(20)

$$W_t=(1-\alpha -\varphi ){\displaystyle \frac{Y_t}{H_t}}$$

(21)

$$H_s=W_s\gamma C_s$$

(22)

$$Y_s=C_s+I_s$$

(23)

4. Data

The data set for the study consists of quarterly observations of real consumption expenditures, real private investments (index), real GDP (index), average hours of work, crude oil production and the real domestic crude oil price. The data set was sourced from the Central Bank of Iran, the National Oil Company and the World Bank. The variable gaps are the gap between the actual variables and their trend using the Hodrick-Prescott methodology.

To estimate the model some parameter calibration is necessary. We partially calibrated the model based on parameter values recommended in the literature.

β = 0.97 Intertemporal Consumption

α = 0.3 Capital Share

φ = 0.65 Labor Share in Production

ϕ = 0.05 Oil Share in Production

δ = 0.06 Capital Depreciation Rate

γ = 0.4 Elasticity of Leisure

m = 0.05 Oil endowment

The calibrated model also requires initial parameter settings. We initialize these parameters based on the estimated model and the parameter setting in the existing literature as follows.

β = 0.99 discount factor

α = 0.33 capital share

δ = 0.025 depreciation rate

θ = 2.0 labor supply elasticity

ψ = 1.5 Frisch elasticity inverse

ρ oil = 0.8 oil shock persistence

σ_oil = 0.01 oil shock std dev

α oil = 0.05 oil share in production

The model combines estimated and calibrated parameters. Parameters that are difficult to identify reliably from the available data or are standard in the RBC literature are calibrated using values reported in previous studies and values consistent with the institutional characteristics of the Iranian economy. The remaining structural parameters are estimated using the observed macroeconomic data. While alternative calibrations could be considered, the purpose of the present study is to evaluate the transmission of productivity and oil supply shocks rather than to conduct an exhaustive sensitivity analysis. Furthermore, not all parameter combinations generate stable and economically meaningful equilibria in DSGE models. Consequently, the selected parameterization focuses on values that are both theoretically plausible and capable of producing a unique bounded solution.

5. Empirical Results

Figure 1 is the graph of key variables. It shows that variables are stationary with a mean of zero, though there are occasional structural breaks in some variables. Graphs support the ADF statistics presented in

Table 1. Summary statistics for the key observed model variables.

Variable	n	Mean	Std. Dev	Min	Max	ADF
c	184	−9.70 ×10−9	11.937	−55.194	34.485	−4.827
i	184	−8.91 × 10−9	10.989	−25.291	28.854	−5.101
h	184	2.69 × 10−10	0.608	−1.854	1.417	−4.948
y	184	4.78 × 10−9	11.019	−35.85	26.938	−4.378
p	184	4.05 × 10−9	16.472	−39.503	34.164	−5.418
o	184	2.42 × 10−8	13.446	−56.072	29.383	−6.405

Notes: All series have been detrended using Hodrick-Prescott methodology. The ADF stands for the Augmented Dickey–Fuller unit root test accounting for break points. n represents the sample size. ^a statistically significant at 1% level based on Vogelsang (1998) one-sided critical values.

.

Table 1 reports summary statistics and ADF unit-root tests for the key variables used in the estimation. The variables exhibit substantial volatility over the sample period. For example, consumption and output display standard deviations of approximately 11 percent, while investment exhibits similar variability, reflecting the sensitivity of the Iranian economy to external shocks, oil-market developments, sanctions, exchange-rate fluctuations, and domestic policy changes. The large ranges between minimum and maximum values further indicate that the sample encompasses periods of significant economic expansion as well as severe contractions. These fluctuations coincide with several major events affecting the Iranian economy, including the Iran-Iraq War, episodes of international sanctions, periods of sharp oil-price increases and declines, exchange-rate crises, and more recent geopolitical tensions. Such events contributed to substantial movements in aggregate demand, investment activity, oil revenues, and household consumption.

The ADF statistics indicate that the variables are stationary, supporting their use within the DSGE framework. Although Figure 1 suggests the presence of structural changes in some series, the DSGE model is less sensitive to structural breaks than conventional reduced-form time-series models because it is based on structural behavioral relationships and forward-looking optimization by households and firms. In this framework, major political and economic events primarily affect the economy through productivity and oil-sector disturbances rather than permanently altering the underlying economic mechanisms. Consequently, the estimated impulse responses capture the dynamic propagation of shocks across different historical episodes.

Nevertheless, the long sample period from 1975 to 2024 encompasses several important regime changes that may influence parameter estimates. Structural breaks associated with the Iran-Iraq War, sanctions episodes, oil-price collapses, and exchange-rate reforms may affect the persistence and magnitude of shocks. Therefore, the results should be interpreted as average responses over multiple economic regimes rather than responses specific to any single period. Future research could extend the analysis by estimating a regime-switching DSGE model or by allowing key structural parameters to vary across policy regimes and sanction periods.

Table 2. Constrained and estimated coefficients.

Coefficients
β	0.97
α	0.3
ϕ	0.65
φ	0.05
δ	0.06
γ	0.4
m	0.05
ρa	0.525 a
	(0.064)
ρz	0.705 a
	(0.052)
σ(e.a)	6.748 a
	(0.352)
σ(e.z)	16.046 a
	(0.837)

Notes: ρ_a and ρ_z are the coefficients in the productivity shocks (a) and oil supply shock (z). σ(e.a) and σ(e.z) represent shocks to productivity and oil supply. ^a statistically significant at 1% level.

presents the calibrated and estimated model parameters and their standard deviations when applicable. All estimated coefficients are statistically significant.

The autoregressive coefficient (persistence) of the technology shock (ρ_a) measures how long a technology shock lasts. A value closer to 1 means shocks die out slowly; a value closer to 0 means they disappear quickly. The value of roughly 0.525 indicates that the shocks to technology do not dye down quickly. The autoregressive coefficient ρ_z gauges the persistence of preference shocks to the state variable Z, i.e., oil supply shocks. The magnitude of this coefficient is indicating that the shocks to oil supply impart a lasting effect on Iran’s economy. Impulse responses, presented below, corroborate this observation showing a long duration of responses to oil supply shocks in consumption, capital formation, wages and real GDP over many quarters.

The standard deviation of the innovation (e_a) to the technology shock process is σ(e.a). It measures the size or volatility of the unexpected change in technology. The standard deviation of the innovation (e_z) to the oil supply shock process is captured by σ(e.z), which measures the volatility of the oil supply (z) shocks.

Table 3. Estimated variances of model variables by the Delta-method of Taylor series approximation.

Variable	Coefficient
c	var(C)	29.922 a
		(7.59)
r	var(r)	100.968 a
		(12.42)
w	var(w)	70.683 a
		(12.64)
h	var(h)	5.784 a
		(0.621)
i	var(i)	1509.953 a
		(169.921)
o	var(o)	512.043 a
		(90.575)
y	var(y)	108.305 a
		(16.788)
q	var(q)	553.014 a
		(80.359)

^a, indicate that Z statistic is significant at 1% level.

presents the variances of maximum-likelihood estimators of the model variables. The delta method is based on a one-step Taylor series expansion of a model variable around its mean. For instance, a function of variable a may be expanded as follows:

f(a) = f(E(a)) + (a − E(a))f′(E(a)); therefore,
Var(f(a)) = Var(a)*[f′(E(a))]².

Table 3 shows that variances are statistically significant. Therefore, in the final estimation, variable distributions are well-defined. The estimation results also indicated that the model reaches stable steady-state values for the model variables. Table 3 shows the model variable steady-state values.

Table 4. Location of model steady-state variables.

Variable	Coefficient
k	3.06
a	1
z	1
c	0.74
r	0.09
w	0.9
h	0.67
i	0.18
o	0.05
y	0.93
q	0.93

presents the location of the steady state values of the model variables confirming that the model settles on a reasonable steady state.

Table 5. Estimated Model Validation For Select Variables.

Variable	Std. Dev	Std. Dev/Std. Dev (y)	Corr with y
Data
c	11.937	1.083	0.821
i	10.989	0.997	0.227
h	0.608	0.055	0.333
y	11.019	1	1
Estimated model
c	5.47	0.526	0.749
i	38.858	3.734	0.926
h	2.405	0.231	0.867
y	10.407	1	1

presents validation of the key model variables. The estimated values of the key model variables and the actual data are qualitatively consistent, further confirming model reliability.

Figure 2, Figure 3 and Figure 4 present the post estimation forecast of the GDP cycle, one step ahead within sample predictions of the GDP and the GDP vs capital cycles. The one step predictions show that the model traces the actual GDP cycle closely. Figure 4 exhibits the within sample close correlation between the GDP growth and the capital stock formation. These observations lend further validation to the estimated model.

Figure 5 summarizes the responses of key variables in the model to productivity shocks and the 95% confidence interval of the shocks represented by the shaded area. Starting with the top left graph, a positive productivity shock raises productivity immediately, and the responses become statistically insignificant in most cases within around 12 quarters or 3 years. Therefore, the economy benefits up to 3 years from positive productivity shocks that normally stem from technological innovations.

The estimated DSGE model shows that a positive productivity shock typically raises the marginal product of labor and capital, leading to higher output and income. In the short run, this boost in output makes households wealthier, encouraging them to consume more. The initial rise in consumption reflects households smoothing their consumption paths in response to anticipated higher lifetime income. This is consistent with the intertemporal optimization behavior assumed in DSGE models, where agents respond to shocks by adjusting consumption and labor supply based on expected future states of the economy.

After about four quarters, consumption may begin to decline even though the initial shock was positive. This decline can be explained by the diminishing marginal effects of the shock over time. As the productivity gains dissipate or the shock fades (as is typically assumed with autoregressive productivity processes in DSGE models), output growth slows, and the temporary boost to income and consumption subsides. Additionally, in response to the productivity shock, interest rates may rise to stabilize inflation or as part of a monetary policy rule. Higher interest rates increase the cost of borrowing and encourage saving over consumption, further contributing to the eventual decline in consumption.

Furthermore, capital and labor also react to the productivity shock. As capital accumulates in response to the higher productivity, the return on capital may fall, and the incentive for firms to expand production could moderate. Simultaneously, the labor market might adjust with a lag, and households might reduce labor supply as their income rises—a wealth effect—leading to lower income and consumption growth in the medium term.

Thus, while a positive productivity shock initially boosts consumption, DSGE dynamics—such as policy responses, intertemporal substitution effects, fading shock persistence, and endogenous factor adjustments—can result in a later consumption decline, even without a reversal of the shock itself.

The impulse response analysis indicate that a positive productivity shock temporarily raises the efficiency with which firms transform capital and labor into output. As productivity increases, the marginal product of capital rises, making investment more profitable. Firms respond by sharply increasing investment in the short run in order to take advantage of higher expected returns. This surge in investment leads to a temporary accumulation of capital over several quarters. However, because the productivity shock is modeled as a transitory autoregressive process, its effect gradually fades. As expected productivity declines, the incentive to sustain elevated investment weakens and firms reduce investment expenditures. Once investment falls below the level required to offset depreciation, the capital stock eventually peaks and begins to decline toward its steady state.

The labor market responses follow a similar dynamic. A productivity improvement raises the marginal product of labor, which leads firms to demand more labor and increases real wages. Households respond to higher wages by supplying more labor hours. As the productivity shock dissipates, both wages and hours gradually fall from their elevated levels and converge back toward their long-run equilibrium values. This adjustment reflects the forward-looking behavior of households and firms in the DSGE framework and the temporary nature of the shock.

The model also generates a response in oil production. Higher overall productivity initially raises oil extraction because the oil-producing sector benefits from improved production efficiency and stronger demand for energy inputs. Oil producers increase extraction to exploit the temporarily higher returns. However, the surge in production is not sustainable. As the productivity shock fades and the economy moves back toward its steady state, demand for energy moderates and incentives for high extraction decline. Additionally, resource constraints and rising marginal extraction costs limit the persistence of high output. Consequently, oil production gradually declines from its post-shock peak.

Real GDP also responds positively to the productivity shock, reflecting the combined effects of higher capital accumulation, increased labor utilization, and greater resource extraction. The impulse responses suggest that the GDP gap relative to potential remains elevated for roughly twelve quarters before gradually dissipating, indicating that the productivity disturbance has meaningful but temporary macroeconomic effects.

These results are broadly consistent with the typical findings in the RBC-DSGE literature. Standard RBC models predict that a positive total factor productivity shock generates a temporary expansion in output, investment, labor hours, and wages. Investment typically rises more strongly than consumption because firms seek to accumulate capital while productivity is high. Capital stock responds with a lag due to the law of motion for capital, rising gradually before returning toward its steady state as investment declines. Similarly, wages and labor supply increase initially because productivity raises the marginal product of labor, but both variables revert as the shock dissipates.

One feature that aligns closely with conventional RBC results is the hump-shaped response of capital and investment. In most RBC calibrations, investment reacts strongly on impact, while capital adjusts more slowly due to accumulation dynamics. The temporary persistence of GDP for several quarters is also consistent with the propagation mechanisms observed in many RBC models, where autoregressive productivity shocks generate medium-term business-cycle fluctuations.

The main distinction between the results described here and the standard RBC-DSGE framework lies in the explicit modeling of oil production. Traditional RBC models typically treat the production sector as a single aggregate technology without explicit resource extraction dynamics. By introducing an oil sector, the model captures how productivity shocks can temporarily raise resource extraction and energy supply before constraints and declining incentives cause production to normalize. This extension reflects the importance of energy production in resource-dependent economies and introduces an additional transmission channel through which productivity shocks affect macroeconomic outcomes.

Overall, the responses observed in the model closely mirror the theoretical predictions of the RBC-DSGE literature. Productivity shocks generate temporary expansions in investment, labor input, and output, followed by gradual adjustments as the economy returns to its steady state. The inclusion of oil production adds an additional sectoral adjustment mechanism but does not fundamentally alter the core RBC dynamics driven by intertemporal optimization and the transitory nature of technology shocks.

Figure 6 presents the IRFs of the key macroeconomic variables in Iran to crude oil market shocks. The estimated DSGE model incorporates oil production as a significant sector. An exogenous positive shock to oil output typically generates immediate and interconnected responses across macroeconomic variables. The nature and persistence of these responses depend on model parameters and the role of oil in the broader economy.

When oil production unexpectedly increases, the immediate effect is a surge in real income for the economy, particularly if oil exports play a dominant role in national revenue. This increase in income often translates into a short-run boost in private consumption. Households, perceiving themselves as temporarily wealthier due to higher oil income or transfers from the government (if oil revenues are taxed or shared), raise consumption levels. In models that include forward-looking agents with imperfect smoothing, this rise may be gradual and exhibit hump-shaped dynamics, depending on expectations about the shock’s duration.

Investment responds positively in the short run, driven by increased profitability in both the oil and related sectors. Firms respond to improved revenue and demand conditions by expanding productive capacity. However, because oil production shocks are often transitory and subject to global market forces, this investment surge tends to peak quickly. Once expectations adjust and diminishing returns set in, investment begins to decline. The capital stock, which accumulates more slowly, rises for several quarters but eventually levels off or declines as new investment slows and depreciation takes effect.

Labor hours and wages are also affected, but the magnitude and direction of the response depend on assumptions about labor market frictions and sectoral rigidities. In models with flexible wages and no segmentation, labor demand increases in response to the output expansion. This drives up hours worked and pushes wages higher. The increase in oil production can also raise the marginal product of labor in sectors connected to energy supply, thereby supporting higher compensation. However, if wages are sticky or labor is immobile between sectors, these adjustments may be less immediate or more muted. As the shock dissipates, labor demand falls, hours decline, and wage growth slows or reverses, especially if the economy faces adjustment costs or reduced profitability.

Empirical DSGE studies offer support for these dynamics. For instance, Bergholt et al. (2019) estimates a DSGE model for a small open oil-producing economy and finds that oil supply shocks generate significant but temporary increases in output, consumption, investment, and wages. Similar results are found by Bjørnland and Thorsrud (2016), who show that positive oil production shocks in Norway lead to increases in employment and wages, but with a decline after the shock fades. Their estimated model captures how forward-looking households and firms adjust behavior not only to current income changes but also to revised expectations about future oil-related revenues.

Overall, oil production shocks act as powerful, yet short-lived stimulants to domestic economic activity. The expansion in consumption and investment, the rise in capital accumulation, and the upward pressure on labor and wages are all consistent with the economy’s attempt to adjust to a temporary improvement in its terms of trade and income. However, the transient nature of such shocks underscores the importance of fiscal stabilization mechanisms and long-term investment strategies to mitigate volatility and smooth the effects on households and firms.

In the context of a calibrated DSGE model, Figure 6 shows that an exogenous positive shock to crude oil supply in Iran has a broad and interconnected impact across macroeconomic variables, including consumption, investment, capital accumulation, labor hours, and wages. These dynamics depend on how oil revenues interact with the structure of the economy, factor markets, and the policy environment.

In the context of a calibrated DSGE model, Figure 7 shows that an exogenous positive shock to crude oil supply in Iran has a broad and interconnected impact across macroeconomic variables, including consumption, investment, capital accumulation, labor hours, and wages. These dynamics depend on how oil revenues interact with the structure of the economy, factor markets, and the policy environment.

This x-axis shows the time horizon, in this case quarters or periods after the shock (from 0 to 20). It shows how GDP responds over time following the oil supply shock. The y-axis represents the magnitude of the GDP response. A response of 3 × 10⁻⁴ means a 0.03% change which is quite small. The response of the GDP to a positive oil supply shock is positive immediately after the shock, peaking at about 3 × 10⁻⁴. It monotonically declines toward zero over the 20-period horizon. This suggests that the effect of the oil supply shock on GDP is temporary and dissipates over time. The lack of oscillation in the IRF implies the shock does not create cycles or rebounds in GDP. It is a standard monotonic decay, common for supply-side responses in DSGE models.

A sudden increase in oil production typically leads to a temporary boost in national income, especially when the oil sector is a significant component of GDP. In many DSGE frameworks that incorporate resource sectors, this positive supply shock increases export revenues or domestic availability of energy inputs, leading to an income effect. Households, in response to higher income or expectations of sustained oil rents, tend to increase their consumption in the short run. This is particularly the case in models where oil income is either distributed directly to households or used by the government to increase spending, thereby fueling aggregate demand.

The response of the consumption to a positive oil supply shock is positive up to seven quarters after the shock, peaking at about 5 × 10⁻⁴. It monotonically declines toward zero over the remaining period horizon. This suggests that the effect of the oil supply shock on consumption is lasting but dissipates over time. The lack of oscillation in the IRF implies the shock does not create cycles or rebounds in consumption. It is a standard monotonic decay, common for supply-side responses in DSGE models.

The response of the investment to a positive oil supply shock is positive immediately after the shock, peaking at about 3 × 10⁻⁴. It monotonically declines toward zero over the 20-period horizon. This suggests that the effect of the oil supply shock on investment is temporary and dissipates over time. The lack of oscillation in the IRF implies the shock does not create cycles or rebounds in investment. Investment also responds positively to the oil production shock, at least initially. The increased income and possibly higher productivity in oil-related sectors raise expected returns on capital, leading firms to increase investment to expand capacity. This capital deepening, however, is typically not permanent. In most DSGE settings, the shock fades, and investment begins to decline once the temporary nature of the oil boom becomes evident or diminishing returns to capital set in.

Capital stock itself builds gradually following the investment response, but because capital accumulates with lag and depreciates over time, the peak in capital stock tends to lag the investment peak. Over time, unless supported by sustained technological improvements or structural changes, capital returns to a path consistent with long-run fundamentals.

The crude oil supply shock that leads to a rise in oil output also raises revenues for the national oil company and the government. This could potentially stimulate business confidence and investment in infrastructure, drilling, and capital projects. The surge in demand for capital leads to higher overall investment in the short run. The investment boom raises the demand for funds in financial markets. If the central bank keeps money supply unchanged, this demand pressure causes a rise in interest rates. Additionally, expectations of higher future output or inflation may prompt the central bank to take steps to counter the inflationary pressures. The IRF of capital to a positive oil supply shocks bears out this scenario.

Labor hours and wages also react to the oil supply shock, but their dynamics depend on model assumptions regarding labor supply elasticity, wage rigidity, and sectoral linkages. In a flexible-wage model with no sectoral segmentation, a positive shock to oil supply raises labor demand. Consequently, labor hours rise, and wages are bid up. However, as the shock dissipates, both labor and wages tend to return to pre-shock levels or even overshoot downward temporarily if the economy experiences adjustment frictions.

Several DSGE studies support these stylized facts. For example, Bjørnland and Thorsrud (2016) develop an oil-augmented DSGE model for an oil-exporting economy and show that positive oil supply shocks raise GDP, consumption, and wages, but the effects are transitory. Similarly, J. P. Medina and Soto (2007), in their DSGE model for commodity-exporting countries, find that oil shocks generate significant short-term increases in consumption and investment, but these effects fade unless structural policies or technological changes are implemented. DSGE models with learning or time-varying oil price expectations, such as those by Bergholt et al. (2019), show even more pronounced but less persistent impacts on macroeconomic aggregates.

While both productivity and oil supply shocks generate positive responses in output, consumption, investment, capital accumulation, labor hours, and wages, the impulse responses suggest important differences in both magnitude and persistence. The productivity shock produces substantially larger responses across most macroeconomic variables than the oil supply shock. For example, the productivity shock raises output by nearly 9 percent on impact and generates particularly strong responses in investment, wages, and oil production. In contrast, the calibrated oil supply shock produces much smaller responses, with output peaking at approximately 3 × 10⁻⁴ and gradually declining toward its steady state. This result suggests that improvements in productive efficiency have a broader and more powerful effect on economic activity than comparable exogenous increases in oil supply.

The stronger impact of productivity shocks is consistent with the RBC-DSGE literature, where technology improvements affect the entire production structure of the economy by increasing the productivity of both labor and capital simultaneously. As a result, productivity gains stimulate investment, labor demand, wages, and output through multiple reinforcing channels. Oil supply shocks, by comparison, operate primarily through an income and revenue channel. Although higher oil production increases export earnings and government revenues, its effects are concentrated in sectors linked directly or indirectly to the petroleum industry and therefore generate a smaller aggregate response.

The impulse responses also reveal differences in persistence. Productivity shocks remain statistically significant for approximately twelve quarters before dissipating. Oil supply shocks display a slower monotonic decay and continue to influence macroeconomic variables for several years. This persistence reflects the structural characteristics of the Iranian economy. Oil revenues account for a substantial share of export earnings, government revenues, foreign exchange receipts, and investment financing. Consequently, an increase in oil production affects not only current income but also government spending, infrastructure investment, credit conditions, and private-sector expectations. These linkages propagate the effects of oil shocks throughout the economy, causing their influence to persist even after the initial disturbance begins to fade.

The persistence of oil-related shocks in Iran is consistent with findings reported for other oil-exporting economies. Bjørnland and Thorsrud (2016) and Bergholt et al. (2019) show that oil production shocks generate prolonged adjustments in output, investment, employment, and wages because oil revenues influence both public and private sector decisions over an extended period. However, our results suggest that although oil shocks are persistent, they are less potent than productivity shocks in generating broad-based economic expansion. This distinction is economically important because it implies that long-run growth is more likely to arise from sustained improvements in productivity than from temporary increases in oil production.

Taken together, the impulse responses indicate that productivity shocks and oil supply shocks are complementary but not equally important drivers of economic activity. Oil shocks provide temporary income gains and stimulate demand, whereas productivity shocks raise the economy’s underlying productive capacity. Consequently, policies aimed at enhancing technological progress, human capital formation, and production efficiency are likely to generate larger and more durable economic benefits than policies that rely primarily on expanding oil production.

6. Broader Economic and Policy Implications

An important consideration in interpreting the impulse response functions is that, under the standard assumptions employed in many DSGE and RBC models, shocks are typically modeled as AR(1) processes and the model is solved using a first-order approximation around the deterministic steady state. Under these conditions, the model exhibits local linearity, implying that positive and negative shocks of equal magnitude generate impulse responses that are mirror images of one another. Consequently, the responses reported for a positive one-standard-deviation shock can be readily extended to the case of a negative shock of identical size and persistence by reversing the sign of the responses. For example, if a positive oil supply shock increases output, consumption, and investment, a negative oil supply shock of the same magnitude would be expected to reduce these variables by a comparable amount. Therefore, while the analysis focuses on positive shocks for expositional convenience, the qualitative implications of the results apply equally to adverse shocks under the maintained assumptions of linearization and symmetric shock processes. Deviations from this symmetry would arise only in the presence of higher-order approximations, nonlinear adjustment mechanisms, occasionally binding constraints, or explicitly asymmetric structural relationships.

The implications of these findings extend well beyond the specific case of oil-exporting economies. Recent global events have demonstrated that disruptions to crude oil supply can have far-reaching consequences for both energy-producing and energy-importing countries through multiple transmission channels. Oil remains a critical input in transportation, manufacturing, agriculture, and electricity generation. Consequently, supply disruptions often lead to higher production and distribution costs, placing upward pressure on inflation, reducing real household income, and weakening economic growth. The effects therefore propagate throughout the global economy regardless of a country’s status as a net exporter or importer of crude oil.

The results also underscore the growing interconnectedness of modern economies. In an increasingly integrated global production system, oil supply shocks are transmitted through international trade, financial markets, and global supply chains. Even economies with limited direct dependence on oil production may experience significant indirect effects through higher import costs, reduced external demand, increased uncertainty, and tighter financial conditions. The economic disruptions associated with recent geopolitical conflicts, sanctions, and supply-chain bottlenecks illustrate that oil market disturbances can generate substantial macroeconomic consequences across a broad spectrum of developed and emerging economies.

From a policy perspective, the findings suggest that policymakers should view oil supply shocks not merely as sector-specific disturbances but as systemic risks capable of affecting inflation, output, employment, and financial stability. Central banks, fiscal authorities, and energy regulators may therefore benefit from incorporating oil-market indicators into their forecasting and risk-management frameworks. Policies aimed at improving energy diversification, enhancing strategic petroleum reserves, increasing energy efficiency, and investing in alternative energy sources may help mitigate the adverse effects of future supply disruptions. Such measures are likely to improve economic resilience not only in oil-exporting countries but also in economies that are heavily dependent on imported energy.

More broadly, the results contribute to a growing literature indicating that energy shocks represent a global macroeconomic phenomenon rather than a country-specific concern. The transmission mechanisms identified in this study are relevant to a wide range of economies and suggest that the economic consequences of oil supply disruptions should be analyzed within a global framework. Consequently, future research may benefit from extending the analysis to groups of advanced and emerging economies in order to assess the extent to which the responses documented here vary across different institutional, energy, and macroeconomic environments.

An important avenue for future research is the incorporation of broader measures of uncertainty into the DSGE framework. While the present study focuses exclusively on crude oil supply shocks, recent research suggests that uncertainty itself may constitute an independent transmission mechanism affecting macroeconomic and financial outcomes. Variables such as the CBOE Crude Oil Volatility Index (OVX), which captures uncertainty in oil markets, and the Global Economic Policy Uncertainty (GEPU) index, which reflects uncertainty surrounding economic policy decisions worldwide, may provide additional insights into the channels through which shocks propagate through the economy. Incorporating such measures could help distinguish between the effects of physical supply disruptions and the effects arising from uncertainty regarding future economic conditions.

Moreover, crude oil supply shocks are often associated with broader episodes of global economic and geopolitical uncertainty. Events that disrupt oil production or distribution frequently coincide with heightened uncertainty regarding inflation, monetary policy, international trade, and economic growth. As a result, part of the response currently attributed to oil supply shocks may, in practice, reflect the influence of uncertainty channels operating simultaneously. Extending the model to include uncertainty indices could therefore provide a richer characterization of economic dynamics and potentially refine the estimated impulse responses by capturing these additional transmission mechanisms.

At the same time, such extensions would substantially increase the complexity of the model and require additional theoretical and empirical considerations regarding the specification and identification of uncertainty shocks. Consequently, while the incorporation of OVX, GEPU, or related uncertainty measures represents a promising direction for future research, it lies beyond the scope of the present study. The current analysis is intended as a first step toward understanding the macroeconomic effects of crude oil supply shocks within a standard RBC-DSGE framework, upon which future studies can build by incorporating uncertainty and other financial-market variables.

7. Summary and Conclusions

This paper aims to analyze how various macroeconomic variables, including consumption, investment, capital accumulation, hours of employment, among others respond to productivity and oil market supply shocks in Iran. To achieve this goal, we estimated an RBC DSGE model of the Iranian economy using data from the first quarter of 1975 to the last quarter of 2024. Furthermore, to examine the robustness of the results we calibrated a similar DSGE model that allowed for oil supply variable to be included.

Positive productivity shocks, which refer to unexpected enhancements in production efficiency, have been linked to economic growth, capital formation, and equity markets. Such shocks can raise output without a corresponding increase in inputs, thus improving the economy’s overall efficiency. Stiroh (2002) shows that positive productivity shocks can have a substantial impact on economic growth, with a one percent increase in productivity leading to a 0.5 to 1 percent increase in GDP. Additionally, these shocks can lead to an increase in capital formation as firms invest in new technologies and equipment to take advantage of the productivity gains.

There are several advantages to investigating oil market shocks in a DSGE framework. First, this model is dynamic and therefore, easily incorporate the time dimension in the decision process of economic agents. Secondly, it embodies the risks and uncertainties that surround economic decisions, especially over time. Finally, the DSGE model is founded on the micro foundations of macroeconomics to study the general equilibrium ramifications of decisions by consumers, firms and investors.

The empirical findings from DSGE models in this paper consistently highlight that positive productivity and oil supply shocks generate immediate expansions in output, consumption, investment, labor hours, and wages. In both productivity and oil-driven cases, households initially raise consumption due to higher income and improved expectations about the future. Firms respond with an investment boom, motivated by temporarily higher returns on capital. Labor demand rises, pushing up hours worked and wages. These outcomes demonstrate strong short-term multipliers, confirming the cogency of DSGE dynamics: forward-looking agents adjust behavior quickly to exploit transitory gains.

However, the common thread across findings is the temporary nature of these gains. Whether the source is a productivity shock or an oil supply shock, responses follow a hump-shaped trajectory, eventually declining as shocks dissipate, diminishing returns emerge, or policy and market constraints intervene. Investment surges lead to capital accumulation, but depreciation and lower future returns cause capital growth to flatten. Labor and wage increases also subside once the exceptional productivity or revenue boost fades. The monotonic decay of impulse responses underlines the transient adjustment process and the inability of shocks alone to sustain long-run growth.

The policy implications of our findings extend beyond the need for short-run stabilization. Because the impulse response functions indicate that the gains from both productivity and oil supply shocks are temporary, sustainable economic growth requires policies that raise the economy’s long-run productive capacity and reduce its vulnerability to oil-market fluctuations.

First, reducing dependence on oil revenues should remain a central policy objective. The results show that positive oil supply shocks stimulate output, consumption, and investment only temporarily. Consequently, oil windfalls should be directed toward productive investments rather than current expenditures. Establishing or strengthening stabilization and sovereign wealth funds could help smooth government spending over the business cycle and reduce the transmission of oil-price volatility to the broader economy.

Second, the productivity-shock results highlight the importance of policies that promote sustained productivity growth. Investment in physical infrastructure, education, workforce training, research and development, and technology adoption can generate more persistent improvements in production efficiency than those arising from temporary resource booms. Policies that encourage private-sector investment, entrepreneurship, and innovation would further enhance productivity and long-run economic performance.

Third, structural diversification is essential. Expanding manufacturing, technology-based industries, agriculture, tourism, and export-oriented non-oil sectors would reduce the economy’s exposure to external oil-market shocks. Diversification would also broaden the tax base and create alternative sources of employment and income, thereby making economic growth less dependent on fluctuations in oil revenues.

Finally, institutional reforms can help transform temporary gains into lasting economic benefits. Improvements in fiscal transparency, regulatory quality, property-rights protection, financial-market development, and governance can enhance the efficiency with which oil revenues are allocated and invested. Stronger institutions would also improve the economy’s ability to absorb external shocks and support sustained capital accumulation and productivity growth.

Our findings suggest that while positive productivity and oil supply shocks can generate significant short-run expansions, long-run economic growth in Iran depends on policies that foster productivity improvements, strengthen institutions, and accelerate diversification away from reliance on the oil sector.