Last updated: Feb. 24, 2021, 7:12 a.m.
Speaker: Dr. Lisa Grace S. Bersales & Dr. Peter Julian A. Cayton
Date: March 11, 2021, 4 p.m.
Venue: via Zoom
Join us in our Professorial Chair Lecture Series via Zoom! You may register here: https://up-edu.zoom.us/meeting/register/tZYkcOmorzMiGd2XxpctTHEFKtoiUN32dNvP.
The lectures will also be livestreamed through Facebook Live at https://www.facebook.com/UPDStat.
Measuring the Contribution of the Informal Sector to the Philippine Economy: Current Practices and Challenges by Dr. Lisa Grace S. Bersales
In the Philippines, demand for a regular estimate of the contribution of the informal sector to the economy is high. Thus, in 2002 and 2003, the highest statistical policy making body of the Philippines then, already provided the official conceptual and operational definitions of “informal sector”. Further, it provided clear criteria for identifying those included in informal sector and listed the exclusions. Various censuses and surveys have then been regularly conducted to provide the data sources for measuring the informal sector. However, since the data sources have not been enough to provide the estimation based on official definition, the Philippine Statistics Authority (PSA), the statistics office of the Philippines, has been reporting on the contribution of the unorganized sector to the economy. The unorganized sector refers to the sector not covered by establishment surveys of the Philippine Statistical System (Virola and de Perio, 2000). The report on unorganized sector is based on an indirect estimation using the Labor Input Method. This paper shall discuss the current methodology for capturing the informal sector and shall present efforts on providing more new data for enhancing the methodology.
Macroeconomic Fundamentals in Range-Based Volatility Models by Dr. Peter Julian A. Cayton
The paper devises a mixed-frequency model for forecasting range-based volatility. The Parkinson range is decomposed into two components: long-run and short-run volatilities. The long-run component is described with a mixed data sampling [MiDaS] regression model and depends on low-frequency macroeconomic variables. The short-run volatility is modelled with a conditional heteroscedasticity structure. Forecast performance is assessed with the realized Parkinson range and is benchmarked and contrasted with a family of conditional heteroscedasticity models. The devised model opens up a new perspective for modelling realized volatility in emerging financial markets where intra-daily data facilities are difficult to access.
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