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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dig-bid-node-130222020-06-04T22:28:38ZMIDAS Modeling for Core Inflation Forecasting 2018-07-31T00:00:00+0000 http://dx.doi.org/10.18235/0001250 https://publications.iadb.org/en/midas-modeling-core-inflation-forecasting Inter-American Development Bank 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 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. Inter-American Development Bank Luis Libonatti Working Papers application/pdf IDB Publications Argentina en |
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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 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 |
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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 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 Inter-American Development Bank MIDAS Modeling for Core Inflation Forecasting |
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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. |
author2 |
Luis Libonatti |
author_facet |
Luis Libonatti Inter-American Development Bank |
format |
Working Papers |
topic_facet |
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 |
author |
Inter-American Development Bank |
author_sort |
Inter-American Development Bank |
title |
MIDAS Modeling for Core Inflation Forecasting |
title_short |
MIDAS Modeling for Core Inflation Forecasting |
title_full |
MIDAS Modeling for Core Inflation Forecasting |
title_fullStr |
MIDAS Modeling for Core Inflation Forecasting |
title_full_unstemmed |
MIDAS Modeling for Core Inflation Forecasting |
title_sort |
midas modeling for core inflation forecasting |
publisher |
Inter-American Development Bank |
url |
http://dx.doi.org/10.18235/0001250 https://publications.iadb.org/en/midas-modeling-core-inflation-forecasting |
work_keys_str_mv |
AT interamericandevelopmentbank midasmodelingforcoreinflationforecasting |
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1809107472214917120 |