arXiv 11 Jul 2024 · Econometrics
arXiv:2407.08510 · PDF · DOI · OpenAlex · Extracted main text
Inflation is one of the most important economic indicators closely watched by both public institutions and private agents. This study compares the performance of a traditional econometric model, Mixed Data Sampling regression, with one of the newest developments from the field of Artificial Intelligence, a foundational time series forecasting model based on a Long short-term memory neural network called Lag-Llama, in their ability to nowcast the Harmonized Index of Consumer Prices in the Euro area. Two models were compared and assessed whether the Lag-Llama can outperform the MIDAS regression, ensuring that the MIDAS regression is evaluated under the best-case scenario using a dataset spanning from 2010 to 2022. The following metrics were used to evaluate the models: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), correlation with the target, R-squared and adjusted R-squared. The results show better performance of the pre-trained Lag-Llama across all metrics.
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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 | Monteforte, L. and Moretti, G (2012) Real–Time forecasts of inflation: The role of financial variables | 1.000 | 9 | 4 | 100% |
| 2 | Ghysels, ., Sinko, A., and Valkanov, R (2007) MIDAS regressions: Further results and new directions | 0.874 | 6 | 2 | 100% |
| 3 | Rasul, K., Ashok, A., Williams, A. R., Ghonia, H., Bhagwatkar, R., K… (2024) Lag-llama: Towards foundation models for probabilistic time series forecasting | 0.874 | 5 | 2 | 100% |
| 4 | Ghysels, ., Santa-Clara, P., and Valkanov, R (2004) The MIDAS Touch: Mixed Data Sampling Regression Models | 0.811 | 4 | 2 | 100% |
| 5 | Knotek, E. S. and Zaman, S (2017) Nowcasting us headline and core inflation | 0.811 | 4 | 2 | 100% |
| 6 | Ghysels, ., Santa-Clara, P., and Valkanov, R (2005) There is a risk-return trade-off after all | 0.737 | 3 | 2 | 100% |
| 7 | Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von… (2022) On the opportunities and risks of foundation models | 0.644 | 2 | 2 | 100% |
| 8 | Kim, J. H (2019) Multicollinearity and misleading statistical results | 0.644 | 2 | 2 | 100% |
| 9 | Shrestha, N (2020) Detecting multicollinearity in regression analysis | 0.644 | 2 | 2 | 100% |
| 10 | Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A.,… (2023) Llama: Open and efficient foundation language models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 31 scored citations.