MIDAS Modeling for Core Inflation Forecasting
Luis Libonatti ()
Additional contact information
Luis Libonatti: Central Bank of Argentina
No 201772, BCRA Working Paper Series from Central Bank of Argentina, Economic Research Department
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
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://www.bcra.gov.ar/Institucional/DescargaPDF/DownloadPDF.aspx?Id=659 English version (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bcr:wpaper:201772
Access Statistics for this paper
More papers in BCRA Working Paper Series from Central Bank of Argentina, Economic Research Department Contact information at EDIRC.
Bibliographic data for series maintained by Federico Grillo ().