MIDAS Modeling for Core Inflation Forecasting

Luis Libonatti

No 9035, IDB Publications (Working Papers) from Inter-American Development Bank

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 naïve 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.

Keywords: Forecasting; Inflation; MIDAS (search for similar items in EconPapers)
JEL-codes: C22 C53 E37 (search for similar items in EconPapers)
Date: 2018-07
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Persistent link: https://EconPapers.repec.org/RePEc:idb:brikps:9035

DOI: 10.18235/0001250

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