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Integrating big data
in the Belgian CPI
Ken Van Loon, Dorien Roels
Geneva, May 2018
http://statbel.fgov.be
Supermarket scanner data
Current method
Experimental results: multilateral methods & splicing options
Web scraping
Footwear
Second-hand cars
Renting student rooms
Hotel reservations
Consumer electronics
Overview
2 http://statbel.fgov.be
Current methodology: “dynamic” method
In production since January 2015
Monthly chained Jevons index
Threshold: 𝑠𝑚+ 𝑠𝑚−1
2 >
1
𝑛 ∗ λ (λ = 1.25)
Imputations
Dumping filters
Outlier filters
SKUs instead of GTIN
Linking relaunches
Scanner data
3 http://statbel.fgov.be
Goal: switch to a multilateral method in 2020
Ongoing research first results
13 months window length when splicing
Scanner data
4 http://statbel.fgov.be
Multilateral methods Splicing & extension methods
GEKS-Törnqvist Movement Splice
Time Product Dummy Window Splice
Geary-Khamis Half Splice
Augmented Lehr Mean Splice
Fixed Base Monthly Expanding Window (FBEW)
Fixed Base Moving Window (FBMW)
Dataset for testing multilateral methods
One retailer: period of 37 months
COICOPs:
01.1 Food (excl. seasonal products)
01.2 Non-alcoholic beverages
02.1 Alcoholic beverages
12.1.3.2 Articles for personal hygiene & beauty products
Around 480 product groups in total
Incl. extra relaunch linking
Scanner data
5 http://statbel.fgov.be
Relaunches
Scanner data
6 http://statbel.fgov.be
Comparison of multilateral methods (full window)
Scanner data
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Comparison of multilateral methods (full window)
Scanner data
8 http://statbel.fgov.be
Results with splicing & extension methods (rolling window = 13 months)
Scanner data
9 http://statbel.fgov.be
Dumping impact: TPD/GK minus GEKS
Scanner data
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Since 2014
R (rvest, Rselenium)
CPI: international train travel, videogames,…
+/- 70 scripts
Daily or several times a week
Web scraping
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Clothing Drugstores
Footwear Books
Hotel reservations Videogames
Airfares DVD & Blu-ray discs
International train travel Supermarkets
Second hand cars Student rooms
Consumer electronics …
Websites largest footwear retailers
Feature of this market:
Products leave market at significant lower price
downward drift
Web scraping – Footwear
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Non-matched model with stratification
Web scraping – Footwear
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Not yet covered
Daily scraping
Time dummy hedonic method (incl. characteristics/depreciation)
Web scraping – Second-hand cars
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Not yet covered
Aggregator sites are scraped
Price
Room size
Specific type of room
Address → geocoding
Web scraping – Renting student rooms
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Geocoding:
Web scraping – Renting student rooms
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Resulting index:
Current research:
Price collection expanded to more cities
More characteristics
Web scraping – Renting student rooms
17 http://statbel.fgov.be
Year City 1 City 2
T 100 100
T+1 102.1 102.3
Domestic destinations: Cities, Seaside, Ardennes
Virtual reservations:
Web scraping – Hotel reservations
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Manual Web scraping
Frequency 1x / month Daily
Reservation 4 weeks before arrival 4 & 8 weeks before arrival
Characteristics Fri – Sun, Double room Fri - Sun, incl. breakfast & free cancellation
Method Sample of hotels Stratification:
Destination
Area
Weeks booked before arrival date
Hotel classification
Price Per hotel Per stratum
Web scraping – Hotel reservations
19 http://statbel.fgov.be
Product characteristics
Time dummy hedonic method
Splicing methods compared with:
Monthly matching Jevons index (MM)
Monthly chaining & replenishment (MCR)
Simulating current official method
Web scraping – Consumer electronics
20 http://statbel.fgov.be
Web scraping – Consumer electronics
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Scanner data
Dynamic method ~ Multilateral methods
No large differences between splicing/extension options
Lehr index deviates
Web scraping
Footwear/Hotels: no large differences classical vs. web scraping
Second-hand cars/Renting student rooms: index calculation possible
Consumer electronics: Use of hedonics
Conclusion
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Thank you!