نشریه اقتصاد و تجارت نوین
سال نوزدهم شماره 1 (پیاپی 62، بهار 1403)

One of the important goals of today's societies is to achieve a happy society. A happy society leads to improvement of sufficiency and satisfaction with life, improvement of growth and economic development. In this regard, one of the factors affecting happiness is governance indicators, so the purpose of this research is to investigate the influence of governance indicators on happiness in a selection of countries in the Middle East and North Africa region in the period of 2006-2020 using the autoregressive distributed interval of panel distribution failures. (ARDL) is The studied countries are divided into two groups of high-income and low-income countries in three models. According to the research results in the first model for countries with per capita GDP and efficiency, the government has led to an increase in happiness. In low-income countries, there is a negative relationship of 0.84 between GDP growth per capita and happiness index. In the second model, for high-income countries, there is a negative and significant relationship between indicators of responsibility and political stability with happiness. For low-income countries, the responsibility of the negative effect and effectiveness of the government has a positive and significant effect on happiness. In the third model, for countries with high GDP, the quality and supervision of the government leads to an increase in happiness, and corruption leads to a decrease in happiness. For low-income countries, the quality of government supervision has led to a decline in happiness. Therefore, by improving good governance indicators, governments can increase people's happiness and their satisfaction with life.
 Governance, Happens, ,Mena, Panel ARDL

The critical question is, if the Phillips Curve really exists, how have economies with simultaneously low and stable unemployment and inflation rates become ever-increasingly a global experience? This research claims that financial development can prevent the formation of any inflation-unemployment trade-off. The financial development multilateral index is calculated by combining banking sector and stock market indicators using Principal Components Analysis and Credit Weights. Findings from the estimation of “Rolling Auto-Regressive Distributed Lags” (RARDL) for countries with different levels of financial development and financial structures from 1979Q1 to 2016Q4 are in support of policymaking for “balanced development of financial infrastructures in the framework of a well-balanced financial structure” for economies that want to target low inflation and unemployment simultaneously. Finally, the findings of this research show that the trade-off between inflation and unemployment is, in its nature, a policy relationship, not a behavioral one. While there is an “apparently” stable trade-off between inflation and unemployment by using the demand-side policies (and the resulting demand-pulled inflation), by using supply-side policies (and the resulting cost-pushed inflation) there will be no trade-off, so from a political point of view, exit from stagflation become possible.

High volatilities of output and inflation in an economy have negative effects on economic growth, and also cause an uncertain economic environment. Economic complexity is one of the factors affecting changes in macroeconomic variables. By increasing economic complexity, the technological structure of production changes, and there is a movement of production resources between production activities. Therefore, this study investigates the relationship between the economic complexity index and volatility of output and inflation using the system of simultaneous equations during 1978-2019. The results of the estimation show that the increase of economic complexity would result in the decrease of output volatility by 0.121% and the decrease of inflation volatility by 0.106%. Additionally, the variables of government expenditure and trade openness, included as control variables in the equation of output volatility, have a positive effect. Moreover, the variable of exchange rate has a positive effect in the inflation volatility equation. From a policy-making perspective, based on the results of this research, it is suggested that economic complexity, through increasing products variety and promoting more specialized and knowledge-based production, can effectively contribute to economic stability and create conditions for further economic growth and development.

Happiness is one of the important goals in life. A review of previous studies shows that several factors affect happiness; including renewable energy consumption and governance quality. Thus, the purpose of this research is to investigate the relationship between the per capita consumption of renewable energy and the governance quality index on the happiness index in a set of selected countries of the OECD in the annual period of 2013 to 2020 and with the help of quantile panel regression. The results show that the per capita consumption of renewable energy has a positive effect in all deciles except the ninth decile and has a statistical significance in all deciles except the eighth and ninth deciles. The governance index has a positive effect in all deciles and there is statistical significance from the fifth to the ninth decile. Gross domestic product per capita positively and significantly affects all deciles. The economic freedom index has a positive effect up to the seventh decile, and only the fifth to eighth deciles have no statistical significance. The Felakat index has a negative effect from the fourth decile and there is statistical significance only in the ninth decile.

This article tries to answer the question of how institutional quality and trade can be effective in tourism development and which institutional factor among the indicators of good governance will have the greatest impact on tourism. For this purpose, the Principal Component Analysis (PCA) method was used to calculate a weighted average for two indices of Institutional Quality and Tourism Development. Then, taking into account the possibility of differences in countries and regions, using Multilevel Regression Modeling (MRM) and estimates in 5 regions of Africa, Arab countries, Asia and the Pacific, Europe, and North America, Latin America and the Caribbean Sea, and in 119 countries were made between 2002 and 2020.The results of the inter-class correlation coefficient showed that most of the differences are between countries and the region in which the countries are located is not the criterion for the differences in tourism development. Also, the year variable has appeared with 0.048 positive and significant influence on the explanatory variable in the model. The results related to the coefficients of the variables showed that institutional quality, trade index, and per capita income will have a positive and significant effect on the tourism index, and inflation will have a negative effect on the development of tourism. Also, among the individual indicators of good governance, Political Stability, and Regulatory Quality with a positive sign and corruption control with a negative sign appeared significantly in the results.

In this paper, we investigate how novel approaches can improve the predictions made by traditional econometric approaches in the field of forecasting. Autoregressive integrated moving average (ARIMA) is known as one of the most widely used methods for predicting economic and financial time series, providing a good framework, especially for short-term linear predictions of time series. However, the assumption of nonlinear effects in time series and the emergence of novel deep learning algorithms, which can extract complex features of time series and model them, have motivated researchers to examine the predictive power of traditional and novel modeling approaches. In this study, two methods are examined for predicting the prices of the four most valuable cryptocurrencies. ARIMA and three approaches in the field of deep learning, including (RNN, LSTM, and GRU), are investigated. In addition, a hybrid model of deep learning and ARIMA has been introduced, which is a combination of the strengths of both models to increase the accuracy of predictions. The results show that the hybrid models perform better in predicting future time series than each of the ARIMA and deep learning models separately. Also, the ARIMA-GRU model has fewer prediction error values than all estimated models.