نشریه مدلسازی اقتصاد سنجی
سال هفتم شماره 3 (پیاپی 27، پاییز 1401)

In this study, it was found that the degree of the industry complexity is related to the industry inefficiency degree and in this way affects the comparative advantage. For this purpose, factory industry data from twelve countries, including six developed countries and six less developed countries for the period 2011 to 2018 were used. By using the panel data method and the fixed effects technique, the impact of industry complexity on the degree of inefficiency was estimated for a set of selected developed and less developed countries. To measure economic complexity to the separation of industry from the average economic complexity of goods in that industry which is on the Harvard Atlas of Economic Complexity website was used. The data envelopment analysis method has been used to estimate the inefficiency degree index in each industry. The results of the research showed that the degree of inefficiency of the industry in developed countries increases with the increase of the complexity of the industry up to the threshold of 0.2 and then decreases. While in less developed countries, the inefficiency degree decreased with increasing industry complexity to the -0.69 threshold and then increased. In addition, the findings showed that less developed countries have a comparative advantage in industries with complexity in the range of -1.1 to 0.48. The comparative advantage in developed countries is in the range of 0.48 to 1.1. The final result is that less developed countries do not have the capacity to produce complex goods due to high inefficiency degrees.

Inflation forecasting is one of the most important actions of economic policymakers and monetary officials in the field of decision-making. Besides, researchers always try to identify appropriate methods for predicting inflation. Considering the non-linearity of macroeconomic indicators due to the shocks caused by business cycles, it would be better for inflation rate to be estimated by nonlinear models. Accordingly, in the current research study, attempts have been to utilize theoretical and nonlinear models, such as NARDL and NARX. Apart from the mentioned models, two other models, entitled ARIMA and NAR were employed as non-theoretical models. In fact, after estimation of Iran's monthly inflation rate in the period of 4/21/2005 to 9/22/2019; the time span of 10/22/2019 to 11/21/ 2021 was examined. The findings of the study indicated that for the short-term time span and long-term span NARX and NARDL models respectively performed well based the RMSE, DM criteria.

The purpose of this study is to investigate the impact of monetary policy through the channel of bank facilities on macro variables in Iran's economy. In order to analyze the results, dynamic stochastic general equilibrium (DSGE) was used in terms of the structure of the banking system in the period of 1370-1399 based on the frequency of seasonal data. In the statistical analysis section, the impact of monetary policy on banking and macroeconomic variables was compared and evaluated. Based on the obtained results, it can be stated that most of the macroeconomic variables have shown the most positive reaction to the shock caused by the banking facilities as a channel for influencing the monetary policy. Based on this, the independence of the Central Bank and not imposing the results of the government's budget policies on the monetary policies of the Central Bank is a solution to make the monetary policies efficient in the medium and long term.

This paper examines volatility transmission across 10 major sectors in Iranian stock market over the period 10/11/2009 to 10/04/2022 using time-varying parameter vector autoregressive (TVP-VAR). Our findings show that first; the total connectedness index is approximately 54 percent, indicating that volatility spillovers across the Iranian major sectors are substantial. Second; among these 10 sectors, “investment” and “base metal” act as net transmitter of risks while “medicine”, “oil product” and “cement” sectors are the greatest receivers of risk. Third; the evidence reveals the presence of lead-lag effect in the investigated network. The greatest stock market sectors play the role of volatility transmitter to other sectors, while the relatively small sectors have no risk spillovers on major sectors. This paper can provide regulatory suggestions for policymakers and risk management for investors.

Structural changes and economic and financial condition change the nature of economic models, and this shows the importance of using models that consider the dynamics of model parameters over time.In this paper, we use factor augmented vector autoregressive models with time-varying coefficients and stochastic volatility to construct a financial conditions index that can track expectations about financial conditions and economic trends. Time variation in the model’s parameters allows for the weights attached to each financial variable in the index to evolve over time. Furthermore, we develop methods for dynamic model averaging or selection that allow the financial variables entering into the FCI to change over time. The results show that the financial conditions in our country have been associated with many instabilities, which has periodically weakened the efficiency of the country's economy by creating imbalances in the financial system of the economy.

Most of the research has been done on predicting the price changes of cryptocurrencies, and the researchers used machine learning algorithms in this field. The purpose of this article is to present an approach to predicting Bitcoin price changes using machine learning algorithms along with data from people's sentiment analysis towards famous people such as Elon Musk on Twitter, Reddit, and Telegram social networks. The data collected for this research is from 2021 and 2022. The distinguishing feature of this research is the use of technical, economic indicators and sentiment analysis simultaneously to predict the price trend. The recursive feature removal method was used to select the feature and in the next step, the results were compared by testing 8 classification algorithms. In this research, the xgboost algorithm showed excellent performance with a record accuracy of 88% in predicting the trend of Bitcoin price changes.