MIDAS Modeling for Core Inflation Forecasting
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.
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| Format: | Working Papers biblioteca |
| Language: | English |
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Inter-American Development Bank
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| Subjects: | Inflation, C22 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes, C53 - Forecasting and Prediction Methods • Simulation Methods, E37 - Forecasting and Simulation: Models and Applications, Forecasting;Inflation;MIDAS, |
| Online Access: | http://dx.doi.org/10.18235/0001250 https://publications.iadb.org/en/midas-modeling-core-inflation-forecasting |
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