Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/202482 
Autor:innen: 
Erscheinungsjahr: 
2017
Schriftenreihe/Nr.: 
Economic Research Working Papers No. 72
Verlag: 
Banco Central de la República Argentina (BCRA), Investigaciones Económicas (ie), Buenos Aires
Zusammenfassung: 
A forecasting exercise is presented to assess the predictive potential of a daily price index based on online prices, compiled by web scrapping by the private company PriceStats in cooperation with a finance research corporation, State Street Global Markets, as a predictor for a measure of the monthly core inflation rate in Argentina, known as "resto IPCBA" and published by the Statistics Office of the Government 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 tested. Various classes of MIDAS models are found to produce a slight boost in terms of out-of-sample predictive performance at immediate horizons when compared to benchmark naïve models and estimators. Additionally, an analysis of intraperiod forecasts, reveals a slight trend towards increased forecast accuracy as the daily variable approaches a full month for certain horizons.
Schlagwörter: 
MIDAS
distributedlags
coreinflation
forecasting
JEL: 
C22
C53
E37
Dokumentart: 
Working Paper

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