Brazil and India are distinct countries in many fields such as culture, politics and religion, but some similar characteristics are noted. When it comes to territorial extension and population, the two countries are classified as emerging and are currently members of the BRICS Summit. The proximity of countries has become evident in the 21st century, where they seek to achieve the same goal, a greater participation in the world order. The trade relationship between them aims at positive impacts on both economies. This paper seeks to present the characteristics of the Brazil-India trade relationship and its implications for Brazil. Part of the scope of this research was the study of the relationship between the Brazil-India Trade Chain and some macroeconomic variables of these countries, for that a nonlinear causality test was applied, and the Factorial Analysis (main components) was also used. Finally, three methodologies were applied to model and forecast the Trade Chain (cross-validated LASSO, Neural Networks and non-tuned Random Forest) and a comparison was made between the metric performances, thus generating a satisfactory result, where was it noted that India presents a growing share of its trade balance on the GDP, which makes this market attractive to Brazil, because there is a significant increase in commercial relations.
Private passenger transport has undergone changes and adaptations in the last 5 years with the rise of start-ups such as Uber, Cabify and 99, which offer their partners (drivers) an income opportunity. During this same period, Brazil experienced a constant increase in unemployed people. Seeking a solution to unemployment, workers found in private passenger start-ups an alternative. Therefore, despite the unfavorable economic scenario, start-ups had a great business opportunity with the unemployed, who until then had in their assets a durable good (automobile) and, for the most part, did not generate financial gain, thus enabling , the realization of the partnership between unemployed and start-up. From this scenario, for the grounding of the study, we use the State Space Models.The results were satisfactory and pointed out that in the modeling of the series containing the evolution of private passenger start-ups in Brazil, the component that represents the unemployed volume (explanatory variable) is statistically significant, corroborating similar studies already published.
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