This paper discusses the opportunities and challenges presented when utilizing scanner data to compile the CPI, with a focus on implementation guidance to national statistical offices. Empirical results for the Australian CPI are examined following the implementation of a multilateral price index method in December quarter 2017.
KrsinichF. The FEWS index: fixed effects with a window splice; non revisable quality-adjusted price indexes with no characteristic information. Paper presented at the ECE/ILO meeting of the group of experts on consumer price indices, 26–28 May 2014, Geneva, Switzerland.
2.
BirdDBretonRPayneCRestieauxA. Initial report on experiences with scanner data in ONS. Report, Office for National Statistics, Newport, UK; 2014.
3.
BöttcherISergeevS. Austrian scanner data project – report multipurpose consumer price statistics: the use of scanner data. Paper presented at the ECE/ILO meeting of the group of experts on consumer price indices, 26–28 May 2014, Geneva, Switzerland.
4.
MüllerR. Scanner data in the Swiss CPI: an alternative to price collection in the field. Room document at the ECE/ILO meeting of the group of experts on consumer price indices, 10–12 May 2010, Geneva, Switzerland.
5.
HowardADunfordKJonesJVan KintsMNaylorKTarnow-MordiR. Using transactions data to enhance the Australian CPI. Paper presented at the 14 meeting of the Ottawa Group, 20–22 May 2015, Tokyo, Japan.
6.
Van LoonKRoelsD. Integrating big data in the Belgian CPI. Paper presented at the ECE/ILO meeting of the group of experts on consumer price indices, 7–9 May 2018, Geneva, Switzerland.
7.
MetcalfeEFlowerTLewisTMayhewMRowlandM. Research indices using web scraped price data: clustering large datasets into price indices (CLIP). Paper presented at the 15 meeting of the Ottawa Group, 10–12 May 2017, Eltville, Germany.
8.
Australian Bureau of Statistics (ABS). An implementation plan to maximise the use of transactions data in the CPI, Information paper 6401.0.60.004, Canberra: ABS; 2017.
9.
KrsinichF. Implementation of consumer electronics scanner data in the New Zealand CPI. Paper presented at the 14 meeting of the Ottawa Group, 20–22 May 2015, Tokyo, Japan.
10.
ChessaAG. A new methodology for processing scanner data in the Dutch CPI. Eurona2016; 1: 49-69.
11.
FenwickD. Exploiting new technologies and new data sources – the opportunities and challenges associated with scanner data. Paper presented at the ECE/ILO meeting of the group of experts on consumer price indices, 26–28 May 2014, Geneva, Switzerland.
12.
BalkBM. On the use of unit value indices as consumer price subindices. In: Proceedings of the 4 Meeting of the Ottawa Group. Washington DC: U.S. Bureau of Labor Statistics; 1998. pp. 112-120.
13.
DiewertWEFoxKJDe HaanJ. A newly identified source of potential CPI bias: weekly versus monthly unit value price indexes. Economics Letters2016; 141: 169-172.
14.
ILO/IMF/OECD/UNECE/Eurostat/World Bank. Consumer price index manual: theory and practice. Geneva: International Labour Organization; 2004.
15.
DalénJ. Unit values and aggregation in scanner data – towards a best practice. Paper presented at the 15 meeting of the Ottawa Group, 10–12 May 2017, Altville am Rhein, Germany.
16.
De HaanJOpperdoesESchutCM. Item selection in the consumer price index: cut-off versus probability sampling. Survey Methodology1999; 25: 31-41.
17.
IvancicLFoxKJ. Understanding price variation across stores and supermarket chains: some implications for CPI aggregation methods. Review of Income and Wealth2013; 59: 629-647.
18.
BalkBM. Price indexes for elementary aggregates: the sampling approach, Journal of Official Statistics2005; 21: 675-699.
19.
IvancicL. Scanner data and the construction of price indices, PhD thesis 2007, University of New South Wales, Sydney, Australia.
20.
IvancicLDiewertWEFoxKJ. Scanner data, time aggregation and the construction of price indexes. Discussion paper no. 09-09, Department of Economics, University of British Columbia, Vancouver, Canada; 2009.
21.
IvancicLDiewertWEFoxKJ. Scanner data, time aggregation and the construction of price indexes. Journal of Econometrics2011; 161: 24-35.
22.
De HaanJKrsinichF. Scanner data and the treatment of quality change in nonrevisable price indexes. Journal of Business & Economic Statistics2014; 32: 341-358.
23.
SilverMHeraviS. A failure in the measurement of inflation: results from a hedonic and matched experiment using scanner data. Journal of Business & Economic Statistics2005; 23: 269-281.
24.
Van der GrientHADe HaanJ. The use of supermarket scanner data in the Dutch CPI. Paper presented at the ECE/ILO workshop on scanner data, 10 May 2010, Geneva, Switzerland.
25.
Van der GrientHDe HaanJ. Scanner data price indexes: The “Dutch” method versus rolling year GEKS. Paper presented at the 12 meeting of the Ottawa Group, 4–6 May 2011, Wellington, New Zealand.
26.
GiniC. On the circular test of index numbers. International Review of Statistics1932; 9: 3-25.
27.
EltetöÖKövesP. On an index computation problem in international comparisons [in Hungerian]. Statiztikai Szemle1964; 42: 507-518.
28.
SzulcB. Index numbers of multilateral regional comparisons [in Polish]. Przeglad Statysticzny1964; 3: 239-254.
29.
GearyRC. A note on the comparison of exchange rates and purchasing power between countries. Journal of the Royal Statistical Society A1958; 121: 97-99.
30.
KhamisSH. A new system of index numbers for national and international purposes. Journal of the Royal Statistical Society A1972; 135: 96-121.
31.
SummersR. International price comparisons based upon incomplete data. Review of Income and Wealth1973; 19: 1-16.
32.
De HaanJVan der GrientHA. Eliminating chain drift in price indexes based on scanner data. Journal of Econometrics2011; 161: 36-46.
33.
CavesDWChristensenLRDiewertWE. The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica1982; 50: 1393-1414.
34.
InklaarRDiewertWE. Measuring industry productivity and cross country convergence. Journal of Econometrics2016; 192: 426-433.
35.
RaoDSP. Weighted EKS and generalized CPD methods for aggregation at the basic heading level and above basic heading level. Joint World Bank – OECD seminar on purchasing power parities, February 2001, Washington DC, U.S.
36.
MelserD. Scanner data price indexes: addressing some unresolved issues. Journal of Business & Economic Statistics2018; 36: 516-522.
37.
BalkBM. Price and quantity index numbers: models for measuring aggregate change and difference. New York: Cambridge University Press; 2008.
38.
DiewertWE. On the stochastic approach to linking the regions in the ICP, Discussion Paper no. 04-16, Department of Economics, The University of British Columbia, Vancouver, Canada; 2004.
39.
RaoDSP. On the equivalence of weighted country-product dummy (CPD) method and the Rao-system for multilateral price comparisons. Review of Income and Wealth2005; 51: 571-580.
40.
DiewertWEFoxKJ. Substitution bias in multilateral methods for CPI construction using scanner data. Discussion Paper 17-02, Vancouver School of Economics, The University of British Columbia, Vancouver, Canada; 2017.
41.
KrsinichF. The FEWS index: fixed effects with a window splice. Journal of Official Statistics2016; 32: 375-404.
42.
De HaanJ. Estimating quality-adjusted unit value indexes: evidence from scanner data. Paper presented at the SSHRC international conference on index number theory and the measurement of prices and productivity, 30 June – 3 July 2004, Vancouver, Canada.
43.
De HaanJ. A framework for large scale use of scanner data in the Dutch CPI. Paper presented at the 14 meeting of the Ottawa Group, 20–22 May 2015, Tokyo, Japan.
44.
AizcorbeACorradoCDomsM. When do matched-model and hedonic techniques yield similar price measures? Working paper 2003-14, Federal Reserve Bank of San Francisco; 2003.
45.
De HaanJKrsinichF. Time dummy hedonic and quality-adjusted unit value indexes: do they really differ. Review of Income and Wealth 2017; DOI: 10.1111/roiw.12304.
46.
LamborayC. The Geary Khamis index and the Lehr index: how much do they differ? Paper presented at the 15 meeting of the Ottawa Group, 10–12 May 2017, Eltville, Germany.
47.
Australian Bureau of Statistics (ABS). Making greater use of transactions data to compile the consumer price index. Information Paper 6401.0.60.003, Canberra: ABS; 2016.
48.
DiewertWE. Axiomatic and Economic Approaches to International Comparisons. in International and Interarea Comparisons of Income, Output and Prices, ed. by A. Heston and R. E. Lipsey. Chicago: University of Chicago Press, 13–87, 2009.
49.
JohansenINygaardR. Dealing with bias in the Norwegian superlative price index of food and non-alcoholic beverages. Paper presented at the 12 meeting of the Ottawa Group, 4–6 May 2011, Wellington, New Zealand.