MIDAS Modeling for Core Inflation Forecasting

Luis Libonatti ()
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Luis Libonatti: Central Bank of Argentina

No 201772, BCRA Working Paper Series from Central Bank of Argentina, Economic Research Department

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 scraping 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: MIDAS; distributed lags; core inflation; forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 E37 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2017-12
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Persistent link: https://EconPapers.repec.org/RePEc:bcr:wpaper:201772

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