جستجوی مقالات مرتبط با کلیدواژه « Modeling » در نشریات گروه « اقتصاد »

Looking at economic education from a young age is an approach that has recently been approved and welcomed in educational circles. In this regard, the aim of the current research is to know the types of Quranic economy in order to extract their descriptive model and to obtain the relevant educational implications for economic education. To achieve this purpose and due to the multifacetedness of this article, the research method is a combination of descriptive phenomenological stages in the stage of referring to the verses of the Qur'an, analysis and coding of data according to the foundation's data strategy with MAXQDA software in three parts: open coding, central coding and selective coding. And the inference method is from the basics in the section of educational implications. In other words, in the first stage, the researchers had a complete reading of all the verses of the Qur'an and then coded, categorized and typified the extracted data and used the extracted final model to infer educational implications. The results of the research indicate that there are 106 open codes related to the economy in the verses of the Qur'an, which are categorized in the form of three selective codes: the principles of private economy, the principles of government economy, and economic description. The above three categories are divided into six core codes in the next step. Finally, the coded data in the form of tables and pictures have been analyzed and educational inferences have been made.

Although herding behavior is mainly defined on the basis of imitation and repetition in existing theories, it is difficult to provide a mathematical model which is able to identify this phenomenon.

Therefore, in this paper, using the Monte Carlo method and stock price data of Tehran Stock Exchange and OTC companies, during the years 2011 to 2019, the herding behavior among the sample companies is investigated. Given that the Iranian capital market is facing the phenomenon of closure, and this can affect the values ​​of price herding, the results are examined with the New York Stock Exchange as a market developed.

The first finding indicates the presence of herding behavior in 29.6% of possible cases in the sample. The second finding indicates the presence of herding at an average of 4.07%. The third finding reflects the increase in the amount of herding along with the increase in absolute return, which shows that as the stock prices change, the values of herding also increases.Also, the results show that the herding behaves almost symmetrically with increasing the absolute amount of stock returns, the amount of herding behavior is first decreasing and then increasing. Accordingly, when price change are slight, the amount of herding is small; But with drastic increases, the average herding behavior also becomes positive and upwards, reaching 16%. This means that as the price of one stock rises, the prices of other stocks also tend to rise, and the sharper the price increase, the greater the amount of imitation of price behavior. A similar trend is observed when prices fall. As prices fall and negative returns intensify, the average rate of herding behavior also increases, and the higher the decline in prices, the higher the rate of herding behavior. As a result, the higher the absolute amount of price return, the greater the amount of herding behavior.The fourth finding of the study indicates the possibility of a relationship between the herding and trading volume. To examine this relationship in the face of a sharp increase in the volume and number of trades, price data are classified into 20 groups based on the number and volume of trades. Then the amount of herding behavior of each group was calculated. The results show that the relationship between both indices of trading and herding is positive and significant and with a sharp increase in the number or volume of trades, herding measure approaches its maximum value. Similar results on the New York Stock Exchange are described below.

Evidence of herding behavior in the New York Stock Exchange also shows that this phenomenon occurs almost twice as much as in the Iranian capital market. Faster and more coordinated dissemination of news and faster reactions to them in NYSE can be the main reasons for this. These results are consistent with a study by Hwang and Salmon (2004) in which the amount of herding behavior in the US market was higher than the South Korean stock exchange. It seems that due to the less trading halts in NYSE, the significant values ​​of price herding in that market mean that the values ​​obtained for the Tehran market are probably not affected by the trading halts and the results can be reliable.

Todays, the importance of taxes as the main source of government revenue has been approved by economic policymakers and therefore, designing an optimal tax system to achieve this subject is essential. Governments are looking for ways to receive their target taxes from taxpayers at the lowest possible cost and at the declaration tax. Taxpayers, on the other hand, seek to postponed their taxes as much as possible and make the most of the effects of inflation. This paper models the game between taxpayers and tax affairs organization. The results show that the Nash equilibrium in the designed game occurs where taxpayers declare their declared tax less than the diagnostic tax and careful handling by tax affairs organization. In the next step, the equilibrium is somewhere that taxpayers continued their protest until the step of the Court of Justice (the dominant strategy) and tax affairs organization agrees with them (the dominant strategy). Finally, the optimal fine rate for the tax affairs organization was determined until taxpayers do not continue their protest to buy time and devalue the amount paid and agree at the initial stage 238.

The aim of this paper is the classification the economic entropy index in a macroeconomic model. Therefore, the descriptive-analytical methodology and econophysics and systems theory were used. The results show that the economic entropy was divided in four sections, shock entropy, respiration entropy, sleep entropy, and entropy of waste. Increasing the economic entropy index due to the scarcity of environmental resources, predicts the likelihood of an economic catastrophe. This will not only bring about economic growth faces serious problems, but the environment poses a serious problem as a place of residence. According to the results, reducing the instabilities and external stresses to reduce shock entropy, developing the appropriate rules for firms to reduce respiratory entropy, policies based on the reduction of physical and social waste to reduce the entropy of waste and policies for the use of production spaces that are not currently in use to reduce sleep entropy recommended.

The firms in duopoly markets adjust their behavior under strategic interactions to attain more market share and try to compete by different policies. In this study, we used non-delayed and delayed forms of differential game model to investigate competition between firms. Regarding quality as one of the state variables and related control variables of two firms, we have compared the results of two models and have discussed about the results of delayed form. When the delay, which is an unfavorable phenomenon, happens, the firms must behave corresponding to the equilibrium relations derived from the delayed differential game model. Effort of each firm to improve its product quality is influenced by either its delay or its competitor’s delay. The firm having less delay is able to achieve higher quality by means of less effort.

Inflation, as one of the economic phenomena, causes many negative social and cultural consequences such as poverty, disproportionate distribution of income and the spread of financial distress, which in turn imposes significant costs on the economy. For this reason, price stability is considered as the main goal of economic planning and policy in all countries. Therefore, it is important to study and predict this macroeconomic variable. In this regard, various predictive models have been developed in competition with each other. One of these methods is evolutionary algorithms, which is a new method for modeling and predicting various phenomena. In the present study, using the Firefly and Cuckoo algorithm, and employing variables that affect inflation, including liquidity, exchange rate, real interest rate, expected inflation and industrial output during the period of 1975-2015, we attempt to model inflation linearly and non-linearly. The results show that the nonlinear model is more suitable for inflation modeling, and the Firefly algorithm is better than Cuckoo algorithm. According to the precision of the non-linear model developed by Firefly algorithm, it can be used to forecast inflation in the future.

The main purpose of this paper is to model the types of energy intensity in Iranian manufacturing industries. For this, by using the very last micro data, we have decomposed the energy intensity into its types at the 3 and 4 digit aggregate level of ISIC classification. After that, we have modeled the energy intensity types by employing a panel data model. Based on the results obtained from the different methods of measuring the numerical index of decomposition, the factors influencing energy intensity are more fitted when employing non-Divisian indices (Laspeyres, Paasche, and Fisher). Among non Divisian indices, the model with the multiplicative Divisia index has the most explanation power. The results from the model estimated also indicate the intensity of labor has a significant and positive effect on energy intensity in the manufacturing industries. In contrast, capital intensity, profit intensity, energy consumption ratio, energy substitution ratio and technology have a significant and negative effect on energy intensity. Furthermore, R&D intensity and firm size influence on energy intensity non-linearly and significantly.

The main aim of this study is to model and analyze the challenge of inheritance allocation remained by a passed-away couple based on the inheritors strategies and objectives by using Game Theory approach. So, by the help of Graph model to analyze the challenges between the inheritors of a passed-away couple based on the laws and regulations in Islamic Republic o Iran. This model contains two kinds of players selected through 64 abstract constructions of the strategies by applying some limitations and analyzed 8 possible cases and finally, 2 cases predicted as more likely. The results represent that this competition has two equilibriums. That is, if the players stand in thie situations, they have no desire to exit. By the coalition of the players, the results will be more appropriate to get more. The findings of the modeling is completely consistent with real results of the challenges.

Brain drain has been a serious problem for Iran and other developing countries for years. Hence, its definition, effects on different fields, causes, and finally control and management methods have been discussed in many aspects and many methods have been presented. This project attempts to identify the causes and present some methods to manage this phenomenon. It introduces a dynamic model based on motivating systems to keep elites and use them. In other words, not only keeping the elites, but also their appropriate application is important. Finally, the article presents some policies and methods based on this dynamic model to manage and control brain drain.

The main purpose of this paper is to evaluate the modeling of the influential factors affecting the deviation and vulnerability of the implementation of general policies mentioned in Article 44 of the Constitution. For this purpose, analytic hierarchy process method was used. Evaluation period was from the notification of law until May 1389. Research data's were gathered from related experts via questionnaire.Four criteria (indicators) were described along with the following eight sub-criteria in the field of deviation from and vulnerability of Article 44. Modeling results showed that the weakness of law enforcement (30.5), weakness of law (26.5), weakness of private sector (23.2) and the empowerment of quasi-governmental sector (19.7) were the most important factors affecting the deviation and vulnerability of law enforcement.

The establishment of the Value Added Tax (VAT) system in Iran is considered a drastic change in tax system of the nation. Such a change would bring about new expectations and requirements by the government, the taxpayers, and other beneficiaries and would subject the taxation system management to new issues to cope with. To establish such a system, the most important condition is to set an appropriate social and economic ground so that a unified management and a flourishing economy could be achieved. The municipalities in our country have made efforts to pave the way for actualizing sustainable revenues, and VAT is one of the most effective ways to achieve it. In effect, the purpose of such a tax system and of passing laws related to the municipalities is to sufficiently inform the revenue collectors and offer better services to the citizens. This article takes a practical approach to the issue and, via reviewing the related literature, provides an introduction to VAT, the calculation whereof, and its modeling and simulation. Then, the article investigates into the relationship between VAT and the municipalities and their financing system.

Systematic risk refers to the possibility of a collapse of an entire financial system or market. After 2008 financial crisis، a significant amount of effort has been directed to the study of systemic risk and its consequences around the world. In this paper with this assumption that larger and stronger markets influence smaller ones، a model inspired by catalytic chemical model is proposed. In chemical reactions، reagents with higher concentrations tend to favor their conversion to products. In order to modulate the conversion process، catalyzers may be used. In this work a mathematical modeling is proposed with bases on the catalytic chemical reaction model. China and India indices of strong markets are taken as being analogous to the concentration of reagents and the indices of smaller markets as (Iran) concentrations of products. The role of catalyst is to model the degree of influence of one index on another. The results show markets influence of china and India stock markets on Iran stock markets is low.

This article attempts to model oil price volatility through use of conditional variance models (GARCH) in the time interval between January 1983 and December 2010. We present an index to measure the uncertainty, using calculated conditional variance parameters. Based on the results obtained, all models used in this study confirm the existence of conditional variance structure for the time series Iranian oil price data.Moreover models such as TARCH & EGARCH were used to assess leverage effects and both models confirmed such leverage effects. The results of GARCH coefficient tests show that in the long run, conditional variance returns to its average level. In addition, the uncertainty index shows a maximal divergence of 8 percent between similar seasons, while all such variances are contained within the domain of between 1 to 8 Percent.

Dynamism, variations in time, is the inherent characteristic of most economical phenomena. In econometrics, ignoring this dynamism may cause many problems due to over simplification of the problems. One such case occurs in application of static regression models to problems with dynamic nature. In so doing, one ignores the fact that the parameters of the model change in time, which may lead to misleading results.In this paper, we intend to exhibit the importance of this negligence in the context of a practical problem, namely modelling the weekly average rate of conversion of us dollar into Iranian currency, Rial.Thus, two approaches of static and dynamic modelling are compared with respect to their efficiency in tracking the path of the variations of the US dollar rate according to various criteria such as MAD and MSE of predictions. Using a dynamic time series model along with required interventions at outlier points superiority of the Bayesian dynamic model is shown.

Time series processes can be classified to three models, linear models, stochastic models and chaotic models. Based on this classification the linear models are forecast able, the stochastic models are unforecastable and the chaotic models are semi forecast able. The previous researches in the modeling and forecasting of the stock price usually try to prove that, the fluctuations of the share prices in Tehran Stock Exchange are not random walks in spite of the existence similarity to the random walks. Indeed the market has a chaotic behavior. This means that, the Efficient Market Hypothesis (EMH) is failed. Therefore by using a complex and powerful models such as artificial neural networks, one can forecast stock prices in Tehran stock market. This paper proposed another approach to modeling and forecasting of the share price. This approach is based on the Stochastic Differential Equations. The modeling is based on the Black- Scholes pricing model. Comparison the simulation result with the linear ARIMA model indicates that the proposed structure provides an accurate next step and the long term share prices and daily returns forecasting.

In this paper, we deal with several time series of share prices and daily returns of different companies which are members of Tehran Stock Exchange. Three prediction methods are used for time series forecasting. The first method is based on the linear models (ARIMA) for short-term and long-term forecasting. The second method is based on the nonlinear neural networks model and the third method is a neural networks model with a special structure. It has been shown that the time series generator process of these companies are complex nonlinear mappings and the methods based on the various linear modelling strategies are unable to identify these dynamics. Also, it has been shown by using the conventional structure of the nonlinear neural networks that one can not obtain a satisfactory result for long term forecasting. Finally, it is shown that the proposed structrure provides accurate next step and the long term share prices and daily returns forecasting.