نشریه مدلسازی اقتصاد سنجی
سال هشتم شماره 4 (پیاپی 32، زمستان 1402)

For the first time, this research investigat the relationship between speculative sector on herding behavior in the Tehran Stock Exchange market by using the (TVP-FAVAR) model and seasonal data from the years (1388- 1) to (1400-4). The results confirm the existence of herding behavior in the in the stock market and it shows the negative effects of the speculative sector impulse responses on the beta-herding behavior. n addition, the effects of impulse responses of other main variables of the model on the behavior of investors were as follows: the effect of beta herding behavior on itself was almost zero that means this variable was not affected by itself, the effects of GDP in the first three periods were neutral and Then in the year 1400, it became slightly positive, the inflation variable with a two-season delay had insignificant positive effects, and then in the following seasons, mainly in the seventh seasonhas been strongly negative, the total stock return variable was almost positive, and finally, the stock liquidity variable had negative effects on the beta herding behavior variable.

Today, the digital economy can be called the main source of growth in any economy, and countries are increasingly looking for solutions to development of the digital economy and the factors affecting it. Also, the process of changes in the structure of the energy system, fluctuations in energy consumption and price and possibility of replacing fuels with each other, saving energy in energy consumption, and also reducing energy intensity have special importance. Therefore, the main purpose of this study was to investigate the impact of digitalization and energy intensity on economic growth in selected MENA countries. For this purpose, indicators of different sectors of the economy and panel data methods including fixed, random and FGLS techniques are used. The study period is 2000-2021. The results of the study showed that in general, the improvement in digitalization, urban population, the added value of the industry sector and the trade openness has a positive effect in economic growth in selected MENA countries, and energy intensity and its simultaneous effect with the digitization index, have a nagative effect in economic growth in selected MENA countries

This article discusses the role of manufacturing in the economic growth of Iran using the logarithmic mean Divisia index (LMDI) method. The study identifies various drivers of industrialization and their impact on growth, including structural changes (productivity of labor and employment share in industrial factories) and employment scale. The LMDI method is used to analyze with a dataset from Iranian provinces between 1381 and 1399. The results show that industrialization has a positive and significant effect on economic growth, with structural changes having a greater impact than employment scale. The study highlights the importance of sustainable industrial development for economic growth in Iran.

The purpose of this article is to investigate the effects of monetary policy through monetary transmission channels (interest channel, exchange rate channel, credit channel and expectations channel) in the short and long term during the period 1386Q1-1396Q4. For this purpose, using the structural vector autoregression model of the effects Monetary policy was examined through channels. The experimental results obtained by estimating the relationships between variables using the SVAR approach based on statistical data show that in the short and long term, the exchange rate channel is the most effective channel in the transmission of monetary policy, so that the variable of expectations has both the most effect in the short term and long-term on this channel, after the exchange rate channel, there are expectations channel, and after that, credits and interest rate channel, which have the greatest impact in the short and long term, respectively.Finally, by examining the relative importance of monetary transfer channels to each other, it can be pointed out that with the passage of time, the influence of the channels will change, so that with price shocks, the expectations channel has the greatest impact in the short term, and the medium term of the effective money and the interest rate channel is effective in the long term.

The effect of crude oil prices on the economic activity of countries has been considered for decades.  In this period of time, the empirical link between oil price and economic activity has been continuously investigated, but the measured results have shown different results and are contradictory.  Therefore, the present study aims to investigate the effect of crude oil prices on the set of economic activities in selected OPEC countries in the period 2000 to 2019.  This research uses the panel data method and the system of simultaneous equations approach by the three-stage least squares (3SLS) method.  The results obtained from the estimation of the considered models indicate a positive and significant effect of crude oil price on the consumer price index and liquidity volume, respectively, at the rate of 221/0 and 200/1.  Also, the results indicating the negative and significant effect of crude oil price on exchange rate and interest rate are -195/0 and -175/0, respectively.

The present study aims to identify the variables affecting the price of cement and provide in- and out-of-sample forecasts using monthly data from 2019:03 to 2023:02 and a vector autoregression (VAR) model. In this regard, according to the results of the Johansen-Juselius cointegration test, the long-term relationship between the variables was confirmed. Then, the long-term and short-term models were estimated, and the error correction coefficient was -0.0858. In the next stage, based on the results of impulse response functions, exchange rate and construction input price index shocks (respectively with a positive effect of 8 and 6.8 percent in the standard form) have been more effective than other model variables on the fluctuations of cement price. The results of variance decomposition also showed that the construction input price index, energy cost, and exchange rate are important in explaining the price of cement. Finally, we estimated in-sample and out-of-sample forecasts. Based on the forecast evaluation criteria, our founding research model can accurately predict the price trend of cement.