" Rentals for Housing: A Property Fixed-Effects Estimator of Inflation from Administrative Data," " Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data,"Ģ018-13, School of Economics, The University of New South Wales. Macroeconomic Dynamics, Cambridge University Press, vol. " Residential Property Price Indices For Tokyo," Diewert, Erwin & Shimizu, Chihiro, 2015.Erwin Diewert,"Įrwin_diewert-2016-6, Vancouver School of Economics, revised. Review of Income and Wealth, International Association for Research in Income and Wealth, vol. " Price Measurement Using Scanner Data: Time‐Product Dummy Versus Time Dummy Hedonic Indexes," Jan de Haan & Rens Hendriks & Michael Scholz, 2021." Productivity Measurement in the Public Sector: Theory and Practice,"Įrwin_diewert-2017-1, Vancouver School of Economics, revised. Journal of Official Statistics, Sciendo, vol. " The Effects of the Frequency and Implementation Lag of Basket Updates on the Canadian CPI," Huang Ning & Wimalaratne Waruna & Pollard Brent, 2017." Measuring Inflation under Pandemic Conditions," " Age, Time, Vintage, and Price Indexes: Measuring the Depreciation Pattern of Houses,"Ģ016-01, School of Economics, The University of New South Wales. Empirical results for this fixed-effects window-splice (FEWS) index are presented for different data sources: three years of New Zealand consumer electronics scanner data from market-research company GfK six years of United States supermarket scanner data from market-research company IRI and 15 months of New Zealand consumer electronics daily online data from MIT’s Billion Prices Project.
#FEWS SCHOOL FULL#
In production, this can be combined with a modified approach to splicing that incorporates the price movement across the full estimation window to reflect new products with one period’s lag without requiring revision. The fixed-effects (or ‘time-product dummy’) index is shown to be equivalent to a fully interacted time-dummy hedonic index based on all price-determining characteristics of the products, despite those characteristics not being observed. The longitudinal information can be exploited to implicitly quality-adjust the price indexes. This article describes the estimation of quality-adjusted price indexes from ‘big data’ such as scanner and online data when there is no available information on product characteristics for explicit quality adjustment using hedonic regression.
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