https://jemr.khu.ac.ir/ Economic Modeling Research en daily 1 Sun, 26 Jul 2026 00:00:00 +0330 Sun, 26 Jul 2026 00:00:00 +0330 https://jemr.khu.ac.ir/article_11615.html Objective: This study examines the effects of economic policy uncertainty, exchange rate, and oil price on inflation in Iran during the period 2008 to 2023. The main objective is to identify the short-term, medium-term, and long-term nature of these effects and analyze inflation dynamics using modern wavelet and machine learning methods. Materials and Methods: Regularized least squares regression with wavelet kernel (WKRLS) and nonparametric wavelet quantile causality (WNQC) are used to analyze nonlinear and scale-dependent relationships between variables. The data include inflation index, economic policy uncertainty (EPU), unofficial exchange rate, and oil price on a monthly basis. The generalized wavelet quantile Dickey-Fuller test (Wavelet-QADF) is also used to examine the stationarity of time series. Results: The results show that key variables of the Iranian economy are stationary in most quantiles and time scales. According to WKRLS estimates, the effect of economic policy uncertainty on inflation is weak in the short run, decreasing but still significant in the medium run, and increasing non-linearly and acceleratingly in the long run. The exchange rate has the greatest impact on inflation, especially in the short run due to the Iranian economy’s heavy dependence on imports. Oil prices also have a significant impact on inflation and its volatility in the long run. WNQC findings show that economic policy uncertainty and exchange rate uncertainty have a stronger effect in the low and middle quantiles of inflation, while oil prices mainly amplify inflation fluctuations in the long run. Conclusion: The findings emphasize the importance of stable economic policies, reducing dependence on oil revenues, and controlling exchange rate fluctuations for managing inflation in Iran. Also, combining wavelet and machine learning methods allows for a more comprehensive analysis of inflation dynamics in different conditions. https://jemr.khu.ac.ir/article_11616.html Climate change and income inequality are intertwined challenges of sustainable development, yet empirical evidence on the role of government size in environmental quality remains inconclusive. Focusing on three carbon-based indicators, territorial carbon dioxide emissions, consumption-based carbon footprint, and production-based carbon footprint, this study examines the relationship between government size and environmental quality and investigates whether income inequality moderates the magnitude and direction of this relationship. Using panel data for 58 countries over the period 1994–2023, the model is estimated based on five-year averages and diagnostic-based estimators, including FGLS and random effects with country-clustered standard errors; PCSE is also reported as a robustness check for the territorial-emissions group. Income inequality is measured using four complementary indicators: pre-tax and post-tax Gini coefficients, the income ratio of the top 20 percent to the bottom 20 percent, and the tenth-to-first decile ratio. The results show that government size, at the average level of inequality, is associated with higher carbon-based indicators and, consequently, lower environmental quality; however, this relationship depends on the structure of income distribution, the type of carbon indicator, and the inequality measure used. The negative interaction terms indicate that income inequality weakens the positive association between government size and carbon-based indicators. The income-heterogeneity analysis for per capita carbon dioxide emissions further shows that this relationship is stronger in countries below the median income level. In addition, the estimated income coefficients are consistent with the environmental Kuznets curve hypothesis, although most observations remain on the upward-sloping segment of the curve. Overall, the findings suggest that improving environmental quality requires coordinating redistributive policies with a reorientation of public spending toward low-pollution sectors and low-carbon infrastructure. https://jemr.khu.ac.ir/article_11617.html The main objective of this study is to analyze the asymmetric effects of oil revenue shocks and the role of latent macroeconomic variables on the inflation of 12 major groups of consumer goods and services in the Iranian economy from 2009 to 2021. To this end, a Generalized Factor Augmented Vector Autoregression (FAVAR) model was employed, which facilitates the integration of extensive economic data and the extraction of latent nominal and real components.The findings indicate that the response of commodity group inflation to oil revenue shocks is inherently asymmetric. Positive oil revenue shocks trigger the Dutch Disease mechanism, leading to a rise in relative prices within non-tradable sectors such as healthcare, housing, and hospitality. Conversely, in the tradable goods sector, the temporary abundance of foreign exchange acts as a curb on price growth in the short term. On the other hand, negative oil revenue shocks exert severe inflationary pressure across all categories particularly import-dependent groups like food and transportation primarily through currency depreciation and structural budget deficits. Furthermore, the extracted latent components show a strong correlation with the nominal and real sectors; the first component (F1), representing the nominal sector, is the primary driver of inflation across most groups, while the second component (F2), representing the real sector (production and employment), plays a moderating role. https://jemr.khu.ac.ir/article_11618.html Despite the growing use of machine learning in credit scoring, many domestic studies still rely mainly on traditional statistical models and static borrower characteristics, while limited attention has been paid to the role of real behavioral, transactional, and repayment-performance data in post-disbursement credit risk monitoring. To address this research gap, this study compares the performance of four stepwise logistic regression models and the LightGBM algorithm in predicting credit default, using data from 119,050 loan facilities granted to individual customers of Resalat Qard al-Hasan Bank during the period between 26 March 2022 and 18 March 20240. The target variable was defined based on repayment delays of more than 90 days, and model performance was evaluated using AUC, Accuracy, Recall, F1-Score, and Balanced Accuracy. The knowledge contribution of this study lies in providing empirical evidence on the effectiveness of real banking data, focusing on behavioral and transactional variables, comparing a classical statistical model with a machine learning algorithm, and assessing model performance in identifying the minority class under imbalanced credit data. The results indicate that repayment-related variables, particularly the number of overdue installments and outstanding debt balance, are the most important predictors of default. Although the fourth logistic regression model achieved a high overall AUC of 0.98, it performed poorly in identifying high-risk customers, with a Recall of only 0.12%. In contrast, LightGBM identified 92.2% of high-risk customers and outperformed logistic regression on imbalance-sensitive evaluation metrics. These findings suggest that, in imbalanced credit datasets, relying solely on AUC and Accuracy can be misleading, while Recall, F1-Score, and Balanced Accuracy are more informative for assessing a model’s ability to detect high-risk borrowers. Therefore, in the post-disbursement monitoring scenario, machine learning algorithms based on behavioral and transactional data can provide a more accurate and reliable framework for credit risk management in Iranian banks. https://jemr.khu.ac.ir/article_11619.html Extended Abstract Introduction Subjective well-being has become an increasingly important concept in welfare economics, happiness economics, and social policy analysis. Unlike objective welfare indicators such as income, employment, consumption, or access to public services, subjective well-being reflects how individuals evaluate and experience their own lives. In societies undergoing economic, institutional, and generational transitions, individuals’ assessment of their living conditions relative to their parents can provide a meaningful indicator of perceived progress or decline. Iraq represents an important context for such an analysis because the country has experienced economic uncertainty, institutional challenges, demographic pressures, and rapid digital transformation. In this setting, internet use may influence individuals’ perceived well-being by expanding access to information, learning opportunities, social networks, public services, and economic prospects. However, the relationship between internet use and subjective well-being is unlikely to be direct, linear, or uniform across all individuals. Background and Innovation The literature suggests that the welfare effects of internet use depend not only on access but also on the intensity, quality, and purpose of use. Internet use may improve well-being by reducing information costs, facilitating communication, supporting learning, and creating new opportunities. At the same time, it may generate adverse effects through social comparison, misinformation, excessive use, or passive consumption of digital content. Therefore, recent studies emphasize the importance of human capital and digital capability in shaping the welfare consequences of internet use. The main contribution of this study is threefold. First, it focuses on intergenerational relative subjective well-being, rather than conventional life satisfaction. Second, it distinguishes active internet use from mere access or satisfaction with access. Third, it examines whether education moderates the association between internet use and relative subjective well-being. Aim and Method The main objective of this study is to examine the relationship between active internet use and intergenerational relative subjective well-being in Iraq, with particular emphasis on the moderating role of education. The dependent variable is an ordinal measure of respondents’ evaluation of their current living conditions compared with their parents’ generation. It takes three ordered categories: worse than parents, the same as parents, and better than parents. The empirical analysis uses micro-level data from the eighth wave of the Arab Barometer survey for Iraq. Given the ordinal nature of the dependent variable and the survey design of the data, the baseline specification is estimated using a survey-weighted ordered logit model. The model controls for age, age squared, gender, household size, urban residence, employment status, household income adequacy, evaluation of current economic conditions, expectations about future economic conditions, trust in government, and governorate fixed effects. To assess the robustness of the results, alternative specifications including ordered probit, different measures of internet use, marginal effects, and post-estimation diagnostics are also employed. Findings The results indicate that the direct association between internet use and intergenerational relative subjective well-being is not uniform across the population. In the baseline models, internet use alone does not show a strong and stable direct relationship with higher relative subjective well-being after controlling for individual, economic, institutional, and regional characteristics. However, the interaction between active internet use and higher education provides evidence of heterogeneous effects. Among individuals with higher education, active internet use is associated with a higher probability of reporting a “better than parents” status and a lower probability of reporting a “worse than parents” status. This finding suggests that education may enhance individuals’ ability to transform digital access and internet use into meaningful opportunities. The results also show that household income adequacy, household size, evaluation of current economic conditions, expectations about future economic conditions, trust in government, age, and age squared are important correlates of intergenerational relative subjective well-being. Governorate fixed effects are jointly significant, indicating that regional differences within Iraq play an important role in explaining variations in perceived intergenerational well-being. The robustness checks further suggest that the relationship between digital engagement and subjective well-being should not be interpreted as a simple universal effect. Rather, the welfare implications of internet use depend on individuals’ human capital and their capacity to use digital resources effectively. Overall, the findings imply that digital policy should move beyond expanding physical internet access alone. Policies aimed at improving subjective well-being through digital transformation should also promote digital literacy, purposeful internet use, skill formation, and the integration of educational and digital development strategies. In particular, strengthening human capital may allow individuals to benefit more effectively from online information, learning resources, communication networks, and economic opportunities.