Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/208113 
Authors: 
Year of Publication: 
2018
Series/Report no.: 
IDB Working Paper Series No. IDB-WP-897
Publisher: 
Inter-American Development Bank (IDB), Washington, DC
Abstract: 
This paper presents a forecasting exercise that assesses the predictive potential of a daily price index based on online prices. Prices are compiled using web scrapping services provided by the private company PriceStats in cooperation with a finance research corporation, State Street Global Markets. This online price index is tested as a predictor of the monthly core inflation rate in Argentina, known as "resto IPCBA" and published by the Statistics Office of the City of Buenos Aires. Mixed frequency regression models offer a convenient arrangement to accommodate variables sampled at different frequencies and hence many specifications are evaluated. Different classes of these models are found to produce a slight boost in out-of-sample predictive performance at immediate horizons when compared to benchmark näive models and estimators. Additionally, an analysis of intra-period forecasts, reveals a slight trend towards increased forecast accuracy as the daily variable approaches one full month for certain horizons.
Subjects: 
MIDAS
Distributed lags
Core inflation
Forecasting
JEL: 
C22
C53
E37
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Working Paper

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