The present chapter intents to present the symbolic time series analysis (STSA) reviewing the recent developments in sciences. Even if there are very few works applied to social sciences, STSA has a potential to be developed. In particular, due to the limitations about historical data, fields such as Economics and Finance need to develop statistical tests to prove their hypotheses. An independence test and a causality test based on STSA are reviewed. They seem to be more powerful, detecting different kinds of nonlinearities compared with the classical tests, usually applied in social sciences. However, there is much work to do with STSA, and social sciences are a fertile field for the development of new powerful tools.
Part of the book: Time Series Analysis and Applications
In the present chapter, we analyze the relation between tourism specialization, income distribution, and human capital in South America between 1995 and 2015. Causality is studied by applying different approaches. On one hand, the panel data Granger causality test and the test proposed by Dumitrescu and Hurlin are conducted. On the other hand, the individual causality test for each country is considered by applying the classical Granger causality and a novel symbolic causality test. The results suggest that tourism specialization measured as arrival/population (TSA) and receipts/exports (TSR) and human capital cause income distribution. The estimated regressions suggest the existence of a Kuznets curve between tourism specialization and income distribution in South America, presenting threshold for TSA equal to 53.20% and TSR equal to 19.98%. Under these thresholds, tourism specialization increases income inequality, but overpassing them the income distribution improves. In addition, human capital has also a positive effect on income distribution.
Part of the book: Tourism