Page 232 - Contributed Paper Session (CPS) - Volume 7

CPS2069 Pamela Kaye A. T.
(Matsuo, 2014), for instance nowcasting of service consumption
15
via Google trends.
United States Fed’s Billion Prices Project (BPP) index is an alternate and
16
unconventional measure of retail price inflation though web-
scraping techniques and scanner code data.
United Kingdom Bank of England created a data council for Big Data and uses
text analytics on social media posts, news, central bank
governor’s speeches, and financial contracts among others, to
assess monetary and financial stability. Another case is the
search data on labor and housing markets through webscraping
of key words such as “jobs”, “unemployed”, “sell house” among
others.

In the case of the Philippine central bank, some of the potential use cases of
Big Data through its Department of Economic Statistics (DES) are the
following:
Current Central Bank Statistics Potential Big Data Sources
Property Prices: Web scraping of online housing prices on
Residential Real Estate Price Index sites such as www.lamudi.ph, Property24.com,
Commercial Property Price Index OLX.ph, Propertyfinder.ph
Household Debt Online credit card transactions

Services Account: Mobile call data record, road censors, credit
Travel Services card transactions on travel-related services,
accommodation-based data
Expectations Survey: Sentiment analysis from social media from
Business Expectations Survey Bangko Sentral ng Pilipinas’ official social
Consumer Expectations Survey media accounts, or posts with related tags;
Text analysis from bank communications (i.e.,
announcement of policy rate decisions)
Labor Market Statistics: Online job search engines such as Jobstreet,
Supply and Demand of Labor JobsDB, PhilJobNet
Average Wage Levels

Other potential applications of Big Data for the Philippine central bank
includes nowcasting of macroeconomic indicators, development of sentiment
indices from social media posts and text mining analysis, early warning system
models through real-time market surveillance to detect financial misconduct
and manage risks.

15 Relates household consumption activities and internet use (i.e., searches related to travel,
dining, etc.)
16 Sources: Federal Reserve Board of Governors/Econresdata
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