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

Pollution is one of the problems of the present century and countries are trying to reduce pollution with international treaties. According to the World Bank's assessment of the damage to Iran's environment, in the annual report on the cost of environmental degradation is about 5 to 10 percent of GDP. According to the World Bank report, Iran is one of the countries with high carbon dioxide emissions, so research on emissions is important. This study for the first time predicts carbon dioxide emissions in Iran and polluting parts, using ARIFMA-FIGARCH method in the period 1971-71. The results show that carbon dioxide emissions in Iran, industry and services will increase.In 2035, carbon dioxide emissions in the country are expected to reach 745876 thousand tons, in the industrial sector to 25.18 percent of fuel consumption and in the service sector to 25.89 percent of fuel consumption.Development programs have failed to reduce carbon dioxide emissions.

The purpose of this study is to apply the system dynamics approach to predict the price of gold in Iran, identify factors affecting the price of gold and simulate the impact of monetary policy on the price of gold in the period 1389-1405. The simulation was performed with Vensim software. The present study simulates the change in liquidity volume, consumer price index and bank interest rates on the gold market in different scenarios. The results show that the price of gold is not only affected by the gold world price and the value of the dollar, but also the control of liquidity and curbing inflation will play a significant role in stabilizing the gold market. The results confirm that the volume of liquidity and the consumer price index have a direct impact and a significant role in increasing the price of gold. The findings also show that changes in bank interest rates have no effect on changes in gold prices.

Today, forecasting of economic and commercial variables as an important scientific field is developing, and forecasting of macroeconomic variables is of special importance for planners, policy makers and economic enterprises. The agricultural sector, as a producer of strategic products and provider of food for the growing population, has a great influence on economic, social and political decisions. Considering the importance of the agricultural sector in Iran as well as the existence of different and uncontrollable influential factors, the researchers who focus on agricultural sector’ growth, try to use methods of forecasting in order to get results close to reality, reduce the prediction errors, and design policies and plans to improve the place of this sector. In this paper, the mixed frequency data-sampling model (MIDAS) has been used to predict the growth of agricultural sector’ value added. Comparison of the model predictions with actual data indicates the predictive power of the model. This model has predicted the growth rate of agricultural sector's value added over the period 2017-2021 by 3.215%, 2.53%, 2.92%, 5.29%, and 5.99%, respectively.

One of the important issues in the economy is the prediction of economic growth. The correct prediction of economic growth has an important role in government policy and economic planning and can help policymakers in future decision making. In this research, nonlinear structure with non-linear data on the economic value added of non-linear models based on adaptive-fuzzy networks (ANFIS) is used to predict the GDP of Iran.To this end, seasonal data on GDP in Iran have been used from spring 1385 to first quarter of 1395 to predict GDP without oil and its growth, divided into three parts: industry, agriculture and services. Neural-fuzzy network modeling has been investigated in three models with different membership functions. Finally, by prioritizing the power of models in prediction, in each of the three sections, the best model is derived according to the error function. The results of the test data for the first nine months of the year of 1395 show the accuracy of the ANFIS model in the prediction. the error values of each type, ANFIS model to predict future values of GDP of non-oil sectors of industry, agriculture and services and for the nine courses forward this index has been predicted. According to the obtained values, the country's non-oil economic growth in 1395 to about 6 percent. this amount in the years 1396 and 1397 respectively 2 and 3.5 percent.

Forecasting tax revenues is vitally important issue for optimal allocation of taxable resources, planning and budgeting in national and regional levels and knowing the potential national participation in public expenditures. The classical optimization based on mathematical methods may not be reliable in real world and mostly inefficient and inapplicable in complicated world due to their restricted assumptions. The smart optimization may help us to find the solution. This essay based on modified PSO methodology .The initial trial based on the data during 1971- 2007 in case of various direct and indirect taxes , and using updated data during 2008- 2012 for final forecasting , to estimate tax revenues for upcoming next three years (2013 up to 2016) by MATLAB software.

The sustainability of the stock market as a result of changes in economic variables is one of the most important issues for economists and investors and stock market activists. Hence, this study base on study of Fouejieu (2017), investigates the sustainability of stock market in the Tehran Stock Exchange (TSE) due to changes in the inflation rate and itsuncertainty in different regimes of the TSE. To this, investigate the relationship between inflation rate and its uncertainty with stock market return uncertainty using the monthly data from 1998:4 to 2016:3, by applying the Markov Regime Switching Generalized Auto Regressive Conditional Heteroskedasticity approach. And then, we calculated the duration of sustainability in the TSE as a result of inflation changes. The results indicate that the inflation rate has a significant positive effect in the high return regime (bull regime) but, this effect is not statistically significant in the low return regime (bear regime). Moreover, inflation uncertainty has asymmetric effects in different stock market regimes. As well as, the findings indicate the probability of sustainability regime 1 (high return regime) is 29 percent and the probability of sustainability regime 2 (low return regime) is equal to 75 percent. Therefore, the forecasting results indicate that the sustainability of the low return regime will be higher than the high return regime and TSE will be come back faster to the normal situation when it comes out of a low return period (bear regime) and entering to the high return period (bull regime).

This paper aims to identify effective aspects of forecasting on city management in order to illustrate opportunities and discover the advantages of available tools with economic approach. In terms of purpose, this research is applied, and in terms of data collection, it is descriptive-survey. Sampling method is purposeful and 10 experts were considered as sample size. Consolidated questionnaire was used for data collection. In order to measure validity of the questionnaire, convergent validity test was used. To check reliability, Cronbach’s alpha coefficient was calculated as 0.793. KMO and Bartlett’s tests, developed mean-variance approach t-test were used. To perform statistical operations, PLS and SPSS 17 were used. In this research, the indexes were identified by using Delphi technique and eight major indexes including quality of life, environmental thinking, transport, compatibility of economic environment, economic infrastructures of the society, economic health of the society, housing, and energy were selected and all hypotheses were confirmed. Finally, three types of forecasting were proposed. This paper tries to present a model for participatory forecast based on urban economy in city management (case study: city of Qazvin). However, three scenarios were proposed considering economic conditions of Qazvin entitled continuation of status, rapid growth and interconnectedness with the global economy, and balanced growth and interconnectedness with the global economy. Therefore, according to these scenarios, practical suggestions were proposed in line with much dynamism of Qazvin urban economy.

This paper examines the important factors in determining inflation and the requirements to achieve single-digit inflation in Iran. For this purpose, this study has implemented the theoretical foundations & empirical studies of inflation, the experience of transition for the countries with high inflation rates, economic conditions of Iran and the appropriate econometric model. As to be expected, to achieve single-digit inflation, budget deficit control policies with an emphasis on cost reduction and increase in tax revenues, controlling the volume of money, privatization, structural reforms, reducing the share of oil production in the country and a national resolution is required. Examining the important factors involved in determining inflation in Iran, and using vector auto regression models, demonstrate that liquidity has the greatest effect on the rate of inflation both in the short and the long run. The other determining factors are budget deficit, tax revenues and GDP growth rate. Based on the estimation model, and assuming the same trend of variables in the past 10 years to be held, the inflation rate for 1394 equals to 23.5%. As a pessimistic scenario, if the same past monetary and fiscal policies are applied, the predicted inflation rate for 1395 will be 41%.

The examination of Gross Domestic Production behavior and some sectoral and macro variables show that latter convey some useful information as leading indicators for forecasting GDP. So, the past information on these indicators helps to reduce the forecast error of GDP. The objective of this paper is to identify the leading indicators and to compare the ability of those in forecasting short run GDP growth. In this regard, fourteen leading indicators in 8 periods are evaluated. Moreover, the forecasts are compared with the ones with univariate ARMA method based on RMSE criteria. The results show the relative outperformance of forecasts with informal exchange rate, although the quality of leading indicators during different period is changing.

Storing the electrical energy in large scale is impossible. So, it is necessary to identify the factors affecting the electricity demand. Researchers have used different methods to forecast the future demand of electricity, among them intelligent methods and fuzzy based methods are more popular. Since ANFIS structure is based on researcher’s experience about phenomenon, the created structure may not have the best result. Therefore, we used PSO-ANFIS structure. In this paper long term electricity demand is forecasted until the year 2025 by hybrid PSO-ANFIS algorithm. The results confirm the high power of the Adaptive Neural based Fuzzy Inference System in forecasting the electricity demand. Results also indicate that the forecasted electricity demand will be 401 billion KWh in 2025. The prediction performance of the proposed technique is more accurate than the ARIMA model.

Adopting monetary and financial policies in economy of each country and their maximum effects depend on the correct recognition of money demand function in that country. On the other hand, stability recognition and anticipating the money demand also can be effective on controlling the mass of money in order to meet economic objectives and minimizing damages caused by wrong performance of monetary system. In present paper, estimation, function stability and forecasting money demand function in Iran during the period from 1971 through 2009 are investigated using ARDL method. Results indicate that there is a long-run equilibrium relationship among this function's variables; and direct relationship of Gross Domestic Product (GDP) and inverse relationship of exchange rate variables and rate of inflation on money demand function are also approved. Also, a stability test was conducted on this function where results suggest that discussed function has long term structural stability and it can be said that reaching long term objectives according to stability of money demand function will be next to possible with a shorter interval by changing the effective variables or applying the appropriate monetary and functional policies.

The aim of this study is to introduce appropriate models for forecasting the gas oil prices in Singapore market which influence the gas-oil price in the Middle East. The used data are on weekly basis and covering the period of (1997-2010).The forecasts were made for 10, 20 and 30 percent ages of the data, separately. The models have been employed for prosecutions included four models of Neural Network and ARIMA model. The 4 selected Neural Networks are Feed-Forward Back Propagation, Cascade Back Propagation, Elman Back Propagation and Generalized Regression. The training functions are also Levenberg- Marquardt and Quasi-Newton BFG. The results that the least forecasting error belongs to the network in the Levenberg-Marquardt training function has been used. The results also reveal that for forecasts of 20 and 30 percent of data, Elman Back Propagation and for the 10 percent of the data Feed-Forward Back Propagation networks have the least forecast error. Moreover, the findings also reveal that Generalized Regression network and ARIMA have the largest forecasting error as compared to the other models. However, the findings of Diebold-Mariano statistics showed no statistically significant difference among the networks with the least forecast error, in respect to forecasting accuracy for the gas-oil price using a combination of 80 percent for training and 20 percent of day a for forecasting, as compared to the data combination, how less forecasting error. Finally, forecasting error of in the case of the best model is around 2 percent.

This article predicts tax revenues in various bases (total, corporate tax, income tax, wealth tax and, goods and services tax) for the time period (1390-91). In order to achieve more accurate predictions, firstly the nature of structural time series in respect of linear, nonlinear and stochastic and complexity of the tax system generating time series have been investigated using tests lyapanov exponent and solidarity. Lyapanov exponent results show a weak turbulence in tax time series in which legal persons enjoy the more sensitivity among the other tax sources. The test results also indicate that the system generating time series correlation on legal entities is more complex than the other sources. The second part of this paper is devoted to forecasting tax revenues but due to exchange rate changes and their impacts on Iran's economy, first the paper examines the possible effects arising from the increased tax revenues using VAR models, then VECM models, the models of low-time series like Box- Jenkin and neural network approach with the multi-input, multiple output were used.

Electricity demand is growing very fast in Iran and it is important to forecast its future demand and its monthly variation accurately.Artificial Neural Network (ANN) is a powerful tool for nonlinear models for forecasting and it was used to estimate monthly electricity demand in this study. In this paper, we compared the Non-linear ANN model with ARIMA linear model to estimate monthly electricity demand for a priod of 3 years. Using MSE, RMSE, NMSE, MHE, MAPE and R2 indicatorss, our results show that ANN forecasting model is superior to ARIMA in terms of less error coefficient and high explanatory ability.

Forecasting employment and unemployment figures for Iran can help depict labor market condition on horizon of vision plan. Using ARIMAmodel, the author has aimed to forecast the share of economic sectors, namely agriculture, industry and mines and service sector, in Iran’s employment over the period of 6009-6062. The findings of this paper indicate that over the past 20years (6723-6003) employment trends have not been uniform for these sectors, i.e., it has been decreasing for agriculture and increasing at a slow rate for industry and mines. For service sectors it has been constantly increasing.The findings further provide the ground to argue that this pattern of developments in employment would be maintained on the horizon of vision plan in 6101(6062).