Gilberto Boaretto, Marcelo C. Medeiros
arXiv 22 Aug 2023 · Econometrics · 3 citations (OpenAlex)
arXiv:2308.11173 · PDF · DOI · OpenAlex · Extracted main text
This paper examines the effectiveness of several forecasting methods for predicting inflation, focusing on aggregating disaggregated forecasts - also known in the literature as the bottom-up approach. Taking the Brazilian case as an application, we consider different disaggregation levels for inflation and employ a range of traditional time series techniques as well as linear and nonlinear machine learning (ML) models to deal with a larger number of predictors. For many forecast horizons, the aggregation of disaggregated forecasts performs just as well survey-based expectations and models that generate forecasts using the aggregate directly. Overall, ML methods outperform traditional time series models in predictive accuracy, with outstanding performance in forecasting disaggregates. Our results reinforce the benefits of using models in a data-rich environment for inflation forecasting, including aggregating disaggregated forecasts from ML techniques, mainly during volatile periods. Starting from the COVID-19 pandemic, the random forest model based on both aggregate and disaggregated inflation achieves remarkable predictive performance at intermediate and longer horizons.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Medeiros, Vasconcelos, Veiga, and Zilberman (2021) Forecasting inflation in a data-rich environment: the benefits of machine learning methods | 1.000 | 8 | 4 | 100% |
| 2 | Garcia, Medeiros, and Vasconcelos (2017) Real-time inflation forecasting with high-dimensional models: The case of Brazil | 1.000 | 7 | 4 | 100% |
| altug2016 | unmatched citation key altug2016 | 0.811 | 4 | 2 | 100% |
| 4 | Bermingham and D’Agostino (2014) Understanding and forecasting aggregate and disaggregate price dynamics | 0.811 | 4 | 2 | 100% |
| 5 | Faust and Wright (2013) Forecasting Inflation | 0.811 | 4 | 2 | 100% |
| 6 | Ibarra (2012) Do disaggregated CPI data improve the accuracy of inflation forecasts? | 0.811 | 4 | 2 | 100% |
| 7 | Bai and Ng (2008) Forecasting economic time series using targeted predictors | 0.737 | 3 | 2 | 100% |
| 8 | Duarte and Rua (2007) Forecasting inflation through a bottom-up approach: How bottom is bottom? | 0.737 | 3 | 2 | 100% |
| 9 | Bai and Ng (2002) Determining the number of factors in approximate factor models | 0.644 | 2 | 2 | 100% |
| 10 | Fan, Masini, and Medeiros (2021) Bridging factor and sparse models self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 48 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 1.4cm bred Maximally Forward-Looking Core Inflation | 0.405 | 1 | 1 |