پژوهشنامه اقتصادی
پیاپی 84 (بهار 1401)

Economic sanctions are a low-cost tool that replaces military action with a high economic, political and human cost. The severity of the effects of sanctions depends on the degree of dependence of the target economy on the outside world, the cooperation of the international community with the ones imposing sanctions, and the potential ability to substitute domestic production with imports. If domestic supply is sufficiently resilient, sanctions act, such as an import substitution policy, can lead to higher growth and more employment. Otherwise, sanctions will act as a lack of domestic supply. This study evaluates the welfare effects of sanctions on basic items - whose rising prices affect living standards. We use a multiple choice model and logit function, the income and cost effects of sanctions as well as the impact of exchange rate changes on the price of basic goods on the welfare standard and the probability of households joining the poor group are considered. The results show that, due to the inelastic domestic supply, the possibility of substituting imports is limited, so the cost effects are dominant for the year 2019, in the effective exchange rate scenario, poverty growth is estimated at %2.2 (about 1828 thousand people, and in the official exchange rate scenario, poverty growth is 3.1 percent (and about 2575 thousand People have joined the poor group).

The purpose of this article is to investigate the effect of behavioral deviations on the pricing of financial assets with the assumption that sentiment is an important and relevant risk factor in the Iranian capital market. This paper also examines the effect of sentiment, momentum, size, value, and market risk premium by estimating the Multi-Factor Asset Pricing Model (APT). In order to perform empirical analysis, the quarterly returns of companies listed on the Tehran Stock Exchange in the period 2010-2019 in the form of 18 stock exchange groups including 63 listed companies are used. Using two indicators of market turnover sentiment.  and sentiment effect. we estimate the sentiment index and by extending the Carhart model and considering two sentiment variables in the form of SAPM model, coefficients estimate by using Hausman-Taylor panel data method. The results of the model show that in the SAPM model, the sentiment variable is very important and significant and have a positive relationship with the average seasonal rate of return of different stock exchange groups.

This study examines the impact of energy security on the economic growth of the 10 selected energy exporting countries in the Middle East. The Benchmark model is based on a generalized version of Cobb Douglass’s production function.  Ten measures of energy security have been used for the whole set of panels, using five concepts of energy security including, availability, accessibility, acceptability, cost- effectiveness and development capability. The paper uses estimated generalized least squares (EGLS), and panel– corrected standard error (PCSE) to estimate the model. Based on the results, the lack of difference between "energy production" and "energy consumption", has a positive effect on the economic growth of selected Middle Eastern energy exporting countries . Also, "national energy supply ability", "national energy structure", "renewable energy consumption", "carbon dioxide emissions from fossil energy consumption", "political stability" and "oil price" also have a positive effect on the economic growth of these countries. But the amount of "energy intensity" and "the ratio of carbon dioxide emissions to GDP" had a negative impact on their economic growth.

One of the most challenging issues in forecasting economic variables is the lack of sufficient data or  the missing data in time series. In this paper, the time series of the GDP growth rate from 1980 to 2019 for 18 Middle East and North African countries is modeled via a generalized network autoregressive model. Of the total observations, 13.42% were missed. In the proposed model, a random network is applied to the data for which nodes represent countries or related time series. Then, an autoregressive model of each node was constructed based on all the data of its multi-stage neighboring nodes. Some parameters of the model may depend on the node (local model) or can be considered the same for all network nodes (global model). The missing data are modeled by changing the weights of the network edges. Finally, the time series was predicted based on the constructed model. Because the network structure affects the model and ultimately the forecast, and on the other hand it is difficult to examine all possible networks, ten thousand un-directional random networks and 16 models including 8 local models and 8 global models on each network are considered. Out of 160,000 models, the network and the model with the least prediction error are selected as the best network and model that are used for the main prediction. The lowest in-sample predictive error was obtained at a local network, which has 64 edges and the number of corresponding model parameters is 4. Finally, the model is compared with the classical models such as AR and VAR. The results indicate the superiority of the proposed method in significantly reducing the prediction error over the AR and VAR models.