Forecasting business cycles is very important in macroeconomic and it is an important part in process of economic decision-making and policy. In recent years, non-linear models have been considered more for forecasting economic variables and application of these models has been made a significant improvement in modeling of the behavior of variables in the area of macroeconomic and particularly financial economics. This article provides a convenient and powerful model for forecasting business cycles by using Smooth Transition Regression ((STR)). The results show that very little error that indicates model performance is acceptable.

This paper examines the asymmetric effect of inflation on the economic growth in Iran during the period 1990:2–2008:2. To that end, we use a Smooth Transition Regression model which allows having a regime change in the relationship between the variables.We found evidence of threshold behavior in the relationship between the inflation rate and the economic growth in Iran; such that, there is a threshold level of inflation which beyond that the relationship between these variables changes. This estimated threshold for the quarterly inflation rate is 4.56. The results also show that there is a positive contemporaneous relationship between the inflation rate and the economic growth in the first regime, where the inflation rate is below 4.56; however, for the periods with an inflation rate higher than 4.56 there is not any significant relationship between the inflation rate and the economic growth. In addition, the effects of the government expenditures and investment are regime-dependent and change over the time.

Regarding the important role of health in economic growth and development, the purpose of the present paper is to investigate the impact of life expectancy, as the most important indicator of health, on economic growth in Iran during 1965-2009. The estimated Smooth Transition Regression ((STR)) model supports a nonlinear threshold behavior in the relationship between life expectancy and economic growth in the country in a two regime (STR)uctures with a threshold level of 55.34 years. In other words, our findings are both consistent with Acemoglu and Johnson (2007) for the negative impact and with demographic Transition theory for the reducing effect of life expectancy on economic growth in Iran. This shows the country is approaching the stage of the fertility Transition, where the increase in life expectancy will bring about a decline in population.

The reaction function of the monetary policy maker can provide insight into the factors influencing monetary policy decisions. Empirical estimates indicate the existence of differences among countries, whether monetary policy only reacts to expected inflation or takes into account expected production developments. This study investigates the reaction function of the Iranin central bank, focusing on the variation in nominal interest rate as the main in(STR)ument for monetary policy. For this purpose, we analyzed  the reaction function of the monetary policy authority for the annual time series period from 2002 to 2019 using nonlinear Taylor rule.Evaluating the behavior of monetary policy makers in response to changes in situational variables; Oil price changes, official exchange rate changes, inflation gap and production gap using Smooth Transition Regression ((STR)) model have shown that, firstly: the reaction function of the monetary policymaker in Iran is non-linear and secondly; It can be seen that by entering the variables of official exchange rate changes and oil price changes into the model, with the increase of the production gap coefficient compared to the inflation coefficient, the inflation targeting scenario changes towards the targeting of production stabilization, and thirdly; The variable influence channel of oil price changes on the reaction function of the monetary policymaker is the official exchange rate gap.

Due to the important role of economic globalization in income inequality of countries, the purpose of this paper is to investigate the non-linear impact of economic globalization on income inequality in Iran during 1979-2014. For this purpose, is used the Smooth Transition Regression ((STR)) model. The estimated Smooth Transition Regression ((STR)) model supports a nonlinear threshold behavior in the relationship between economic globalization and income inequality in the country in a two regime (STR)uctures with positive effect and a threshold level of about 26/15%. so that increases the intensity of this positive impact with crossing threshold level and entering the second regime.

This question has always been raised whether economic growth can lead to the reduction of environmental pollution. In this regard, the present study investigats the relationship between economic growth and environmental pollution in Iran during the period 1984-2021, considering per capita GDP and per capita ecological footprint as indicators of economic growth and environmental pollution, respectively. The results obtained from applying the Smooth Transition Regression ((STR)) approach, with the Gini income inequality index as the Transition variable, indicate that economic growth affects the per capita ecological footprint within a two-regime framework. According to the results, in the first regime, when income inequality is below the threshold level of GINI* = 0.418777, increased economic growth leads to a reduction in the ecological footprint. However, in the second regime, when income inequality exceeds this threshold, economic growth leads to an increase in the ecological footprint. The results suggest that if income inequality is below the threshold level of GINI* = 0.418777, implementing macroeconomic policies aimed at improving economic growth can lead to a reduction in environmental pollution. Therefore, it is recommended that economic planners and policymakers seriously pursue income inequality reduction policies alongside growth policies to achieve a reduction in environmental pollution.

This study investigates the relationships between Bitcoin (BTC) prices and fluctuations in relation to gold, USD, and Iran's oil prices from 2019 to 2022. We employed the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) method to model the fluctua-tions of financial variables. Additionally, the Smooth Transition Regression ((STR)) method was applied to explore the relationships between the variables. The results reveal significant positive correlations between BTC prices and gold, as well as oil, and a negative correlation with USD prices. We observed volatility persistence, causality, and phase differences between BTC and other financial in(STR)uments and indicators. Notably, a negative relationship was identified between Bitcoin and the USD in both linear and non-linear aspects, with a larger coefficient in the second regime. Furthermore, a posi-tive relationship was found between Bitcoin and the variables of gold and oil prices, with coefficients being larger in the second regime compared to the first.

in the present study, an attempt will be made to investigate this effect in the form of non-linear soft transfer Regression methods during the period of 1991 to 2022 for Iran's economy. The results of the estimations showed that the primary effects of government debts on the poverty index were negative and significant, but the secondary effects were positive and significant, so the overall result showed that the increase in government debts led to an increase of 2.801 percentage will be in poverty. This issue is due to the fact that the government, by increasing its debts, makes spending policies difficult in terms of size and composition, then due to compensating these expenses, it also involves tax policies, which in the long run can make regulatory policies ineffective and lead to financial pressure on the middle class and the poor. Inflation rate and population growth in both regimes had positive and significant effects on poverty. Also, the effects of governance variables, human development and GDP growth in the first regime were positive on poverty, but in the second regime, these effects were negative and reduced poverty.

Among the important policy objectives and economic programs, the implementation of policies for reducing the trade deficit. Empirical studies provided conflicting results regarding the effect of devaluations on the trade balance reached. This study aimed to investigate the nonlinear relationship between the real effective exchange rate and trade balance, considering the savings rate using a Smooth Transition Regression approach during period 1960-2014. The results of Smooth Transition Regression ((STR)) model estimation, confirmed that the savings rate had non-linear effect on the trade balance. Our estimation results showed that effectiveness of savings rate, the real effective exchange rate and terms of trade on trade balance depended to the regimes which Iranian economy was in there. Also we fund that generally in first regime savings rate and the effective real exchange rate had statically meaningful and positive effect on the trade balance and the terms of trade had a statically meaningful and negative effect on the trade balance. While crossing the threshold and entering into the second regime savings rate and terms of trade coefficients had statically meaningful and positive effect, but the real effective exchange rate had a statically meaningful and negative impact on the trade balance.

Capital flight has become a critical challenge for many countries, directly affecting economic growth and development. Uncertainty about economic policies often prevents investors from forming clear expectations, prompting them to seek politically and economically stable environments. Consequently, political, economic, and financial risks are among the most influential factors driving capital flight. This study investigates the threshold effects of country risk on capital flight in Iran during 1984–2022. Using unit root and linearity tests, the Smooth Transition Regression ((STR)) model was employed to estimate the relevant relationships. Results show that composite risk has a positive and significant effect on capital flight in both regimes, with its impact intensifying once the risk exceeds a threshold. Additionally, the real interest rate negatively influences capital flight, but significantly only in the second regime. The stock market index positively affects capital flight, but this effect is significant only in the first regime. These findings highlight the importance of political, economic, and financial stability in reducing capital outflows.