Showing 5 results for Hosseini
Dr Hasan Hosseini Nasab, Hasan Rasay,
Volume 3, Issue 9 (10-2012)
Abstract
In this paper a new model for optimal investment in advanced manufacturing machines is proposed, using fuzzy linear programming. In the first step decision-makers determine the strategic objectives of the company and their minimum acceptable achievement levels, using fuzzy numbers. Thereafter, feasible alternatives and their degree of influence to the achievement of each objective are concluded in the form of linguistic variables. To construct the model, the degree of influence of each alternative in the achievement of the objectives are considered as technological coefficients, and the minimum level of acceptance of objectives are considered as constraints (right hand side variables). Furthermore, the mutually exclusive alternatives, the interaction between machines and the constraint of limited investment of budget are included in the model. The aim of the model is to determine the number of machines that needs to be purchased in order to maximize the present value of investment. The calculation of net present value is executed based on discount cash flow, inflation rate, interest rate, revenue and costs of each machine on a fuzzy environment. Finally by presenting an empirical illustration, the performance of this model is clarified.
Vahid Majed, Hossein Mirshojaeian Hosseini , Samira Riazi ِdoust,
Volume 10, Issue 35 (3-2019)
Abstract
Homogeneity of groups in studies those use cross section and multi-level data is important. Most studies in economics especially panel data analysis need some kinds of homogeneity to ensure validity of results. This paper represents the methods known as clustering and homogenization of groups in cross section studies based on enviro-economics components. For this, a sample of 92 countries which produce the most greenhouse gases including CO2, clustered based on 18 criteria. Those criteria reduced to five primary components using factor analysis. Clustering of countries done by HCPC (Hierarchical Clustering on Principal Component) method. All 92 countries were clustered in 7 different groups. For each group properties of countries indicates the homogeneity of each cluster. In cross section analysis with many sections, especially analysis based on panel data, clustering, increases assurance of expected homogeneity and validity of result.
Ghasem Palouj, Seyed Fakhreddin Fakhrhosseini,
Volume 14, Issue 54 (2-2024)
Abstract
This study explores how monetary and fiscal policies influence certain macroeconomic variables through a multi-sector stochastic dynamic general equilibrium (DSGE) model that includes input-output (IO) analysis. The focus is on the industrial sector, taking into account the specific conditions for Iran. The research uses quarterly data from Spring 2006 to Spring 2023 and references the 2016 input-output table provided by the Central Bank. In the nonlinear model, the original 89 activities from the input-output table have been simplified to 9, which includes the industrial sector and eight other sectors. Model parameters are estimated based on previous studies of the Iranian economy and data from the input-output table. The model's effectiveness is assessed by comparing simulation results with real-world data, which shows a strong correlation. The simulations indicate that increases in the money supply result in only a small rise in both total and industrial output. This leads to a slight decrease in total employment, while employment in the industrial sector experiences a minor increase. Similarly, increases in government spending show tiny improvements in overall and industrial output, accompanied by a slight drop in total employment and a small rise in the industrial sector. The findings suggest that the effects of monetary and fiscal policy shocks on output and employment, when accounting for input-output relationships and dividing the economy into nine sectors, better reflect the realities of the Iranian economy. Given the minimal influence of these policies on boosting production and economic growth, it is essential for them to be targeted and supported by additional measures and strategies.
Seyed Fakhrodin Fakhrehosseini, Dr Meysam Kaviani,
Volume 15, Issue 55 (5-2024)
Abstract
Predicting financial asset volatility is highly important because this information can help investors make more informed decisions regarding buying and selling. Accurate predictions can also reduce financial risks and identify profitable opportunities. Ultimately, the ability to forecast market changes improves portfolio management strategies and minimizes unexpected losses for investors. This study examines and predicts Bitcoin price volatility by using innovative data analysis models. The Heterogeneous Autoregressive (HAR) model and its variants were selected as the primary tools for modeling volatility because of their high capability to analyze volatility data across different time scales. Given the unique characteristics of cryptocurrency markets and rapid, unpredictable price fluctuations, the use of models that can simultaneously capture both short- and long-term volatility is of significant importance. In this study, high-frequency historical Bitcoin price data from 2018 to 2022, covering 60-minute, daily, weekly, and monthly intervals, were analyzed using the HAR, HARJ, HARQ, and HARQJ models. The results indicate that heterogeneous models have strong predictive power for Bitcoin price volatility, and incorporating jump factors into these models further improves their forecasting accuracy.
Predicting financial asset volatility is highly important because this information can help investors make more informed decisions regarding buying and selling. Accurate predictions can also reduce financial risks and identify profitable opportunities. Ultimately, the ability to forecast market changes improves portfolio management strategies and minimizes unexpected losses for investors. This study examines and predicts Bitcoin price volatility by using innovative data analysis models. The Heterogeneous Autoregressive (HAR) model and its variants were selected as the primary tools for modeling volatility because of their high capability to analyze volatility data across different time scales. Given the unique characteristics of cryptocurrency markets and rapid, unpredictable price fluctuations, the use of models that can simultaneously capture both short- and long-term volatility is of significant importance. In this study, high-frequency historical Bitcoin price data from 2018 to 2022, covering 60-minute, daily, weekly, and monthly intervals, were analyzed using the HAR, HARJ, HARQ, and HARQJ models. The results indicate that heterogeneous models have strong predictive power for Bitcoin price volatility, and incorporating jump factors into these models further improves their forecasting accuracy.
Ali Moridian, Hassan Heidari, Seyed Mehdi Hosseini, Heshmatollah Asgari,
Volume 16, Issue 60 (9-2026)
Abstract
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.
