نشریه اقتصاد و الگوسازی
پیاپی 58 (تابستان 1403)

Fluctuations of economic growth and inflation pose significant challenges for national economies, highlighting the importance of understanding their underlying causes. While some studies have explored the role of interest rates in economic fluctuations in Iran, none have specifically utilized the interbank interest rate, which provides a more accurate measure than deposit or loan rates. This study aims to analyze the impact of structural shocks -specifically those from the interbank interest rate, liquidity, and oil revenue- on economic growth and inflation in Iran. Using seasonal data from 2008:2 to 2024:1 within a structural vector autoregressive model, the findings reveal the following: First, an interest rate shock (interbank interest rate) significantly reduces economic growth in the short run, though the effect becomes negligible in the long term. Second, oil revenue and liquidity shocks have a positive impact on economic growth in the short term, with oil revenue exerting a more stable and pronounced influence than liquidity. Third, an interest rate shock causes a significant rise in inflation in the short term; and fourth, both oil revenue and liquidity shocks lead to increased inflation, with the inflationary effect of liquidity shocks being more persistent over time. Since liquidity shocks only affect economic growth in the short term and lead to inflation in the long term, and interest rate shocks also have a negative impact on economic growth in the short term and exacerbate inflation in the long term, it is recommended to follow monetary rules and avoid discretionary policies.

Monetary policies and factors influencing liquidity creation are among the key drivers of inflation in the economy. Various elements play a role in the process of liquidity creation and analyzing the impact of each can greatly aid in controlling inflation and shaping macroeconomic policies. This study investigates the effects of different components of liquidity on inflation in Iran during the period 1981-2021, employing Bayesian averaging methods. Using three Bayesian averaging techniques -BACE, BAS, and BMA- the study identifies the main factors influencing inflation. Subsequently, inflation models are estimated using JMA and MALLOWS methods. Robust checks, conducted via machine learning techniques, confirm the findings of JMA and MALLOWS. The results reveal that among the six key factors contributing to liquidity creation, the most significant drivers of inflation are commercial banks overdraft, the money supply, and inflation expectations. Across all methods employed, the findings indicate that the net debt of government to the central bank has a far smaller inflationary effect compared to the aforementioned three factors. Additionally, growth in quasi-money and the central bank's foreign assets exhibit anti-inflationary effects.

One of the main challenges when studying economic sanctions is the indexation of sanctions. Without a specific index that clearly shows the effects of sanctions, economic modeling does not provide reliable results. This article quantifies the effects of sanctions against the Iranian economy on the consumer price index (inflation) over the period 1979-2022. For this purpose, a quantitative index was used to measure the strength of sanctions and compare their severity with each other using mathematical calculations. Then, by using the Structural Vector Autoregression model and Vector Error Correction model, the effects of sanctions on inflation in the Iranian economy and its amount were separated from other effective factors is measured. The main goal of this article is to answer the question “Do sanctions have an effect on inflation in the Iranian economy?” The results indicate that sanctions have contributed to an increase in the general consumer price index (inflation) of 7.9 percent. This shows that in inflation targeting, in addition to variables such as liquidity volume, interest rate, etc., the policymaker should pay special attention to the quantification of the severity of sanctions.

One of the characteristics of oil-exporting countries like Iran is the dependence of the economic structure on the income from oil exports, which causes the Iranian economy to be affected by the conditions of recession or boom prevailing on the world economy. In this case, extreme oil price fluctuations can affect inflation. In this research, the time-frequency characteristics of the oil price-inflation relationship and oil price-inflation volatility with wavelet analysis approach and monthly data are investigated for the period 1980-2020. The results show that there is a positive, in-phase and significant coherence between inflation and oil prices in the short term. However, in some years, a negative and out-of-phase correlation has been established, so that with the passage of time and in the medium and long term, the coherence between the two series has disappeared. Also, there is a positive and significant correlation between inflation and oil price volatility and by controlling the effect of liquidity growth, the correlation of oil price and oil price volatility with inflation has intensified. Therefore, it is suggested to reduce the country's dependence on oil revenues to the minimum possible amount.

Adopting appropriate exchange rate policies in developing countries is always controversial. In order to prevent losses from changes in exchange rates, monetary policymakers have always sought to find a suitable method for forecasting exchange rates. However, the political and economic characteristics of the exchange rate have caused complex and nonlinear behavior, indicating the use of better models in forecasting. In this study, the optimal exchange rate model was simulated using metaheuristic algorithms. For this purpose, from 257 monthly data of exchange rate, inflation rate, OPEC basket oil price and gold coin price during the period from December 2000 to March 2021, the data were first divided into two groups of training and testing. Each of the metaheuristic algorithms was run for the next 24 months with the parameters related to each algorithm. In each run, the value of the error coefficients was selected after reaching the stop criterion and finally the best algorithm was selected based on the highest convergence. The results show that the particle swarm algorithm had very good convergence in low number and iteration and had a more accurate performance in forecasting the exchange rate. Given that metaheuristic algorithms are capable of optimizing various processes such as time management, resources, and facilitating daily planning, this study suggests that policymakers use this algorithm to predict the exchange rate before planning in sectors.

Globalization as a complex and multi-dimensional phenomenon has wide-ranging effects on various economic, political, social and environmental areas. In recent decades, its importance has increased as one of the most important issues in policymaking and decisions among global communities. Therefore, the purpose of this research is to investigate the impact of globalization dimensions on economic growth and environmental quality in Iran during the period of 1971-2021 using the wavelet method. Based on the results of the globalization and economic growth index, initially, there is a relatively high dependence on high frequencies during the middle period under review, and the two indices are in the same phase (in the same direction) and economic growth is the reason for the increase globalization. Economic, political and social globalization lead to a decrease in the quality of the environment. The findings also show that in order to improve the quality of the environment and achieve sustainable development, policy makers should pay attention to reducing its negative effects on the environment in formulating globalization policies and adopt strategies to reduce CO2 emissions.