Temporal aggregation and bandwidth selection in estimating long memory

This paper reinterprets results of Ohanissian et al (2003) to show the asymptotic equivalence of temporally aggregating series and using less bandwidth in estimating long memory by Geweke and Porter-Hudak’s (1983) estimator, provided that the same number of periodogram ordinates is used in both cases. This equivalence is in the sense that their joint distribution is asymptotically normal with common mean and variance and unity correlation. Furthermore, I prove that the same applies to the estimator of Robinson (1995). Monte Carlo simulations show that this asymptotic equivalence is a good approximation in finite samples. Moreover, a real example with the daily US Dollar/French Franc exchange rate series is provided.

Transformation of brain drain of brain gain: incentives to attract talented workforce

The key for the future of any country, firm or group lies in the talent, skills, experience, knowledge and capabilities of its people. Migration of human capital resource on an international level depicts the impact on the developing country having its highly educated individuals migrating to developed countries known as “Brain Drain.” Therefore, evaluation of short-term and long-term talent needs and impacts on any country is critical. This paper aims to complement the existing theoretical brain drain and brain gain literature, focusing on the interaction between investment in education, training, healthcare and government to attract highly talented individuals to a developing a country. The migration study is inclusive of the analysis of the highly talented resources that have committed to or are planning to resettle in their developing native countries after investing in themselves through education. The motivational factors of these highly talented individuals are evaluated to determine key needs and drives attracting these individuals back to China from a developed country (aka. reserve migration).