Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar
arXiv 1 Jul 2024 · Econometrics · 11 citations (OpenAlex)
arXiv:2407.00890 · PDF · DOI · OpenAlex · Extracted main text
This paper presents a comparative analysis evaluating the accuracy of Large Language Models (LLMs) against traditional macro time series forecasting approaches. In recent times, LLMs have surged in popularity for forecasting due to their ability to capture intricate patterns in data and quickly adapt across very different domains. However, their effectiveness in forecasting macroeconomic time series data compared to conventional methods remains an area of interest. To address this, we conduct a rigorous evaluation of LLMs against traditional macro forecasting methods, using as common ground the FRED-MD database. Our findings provide valuable insights into the strengths and limitations of LLMs in forecasting macroeconomic time series, shedding light on their applicability in real-world scenarios
appendix boundary found by appendix_command · 77% of the source is main text. Read the extracted text to check this.
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 | Chan, J. C. C (2022) Asymmetric conjugate priors for large bayesian vars | 0.843 | 5 | 3 | 60% |
| 2 | Banbura, M., D. Giannone, and L. Reichlin (2010) Large Bayesian vector autoregressions | 0.811 | 4 | 2 | 100% |
| 3 | Das, A., W. Kong, R. Sen, and Y. Zhou (2024) A decoder-only foundation model for time-series forecasting | 0.811 | 4 | 2 | 100% |
| 4 | Korobilis, D. and D. Pettenuzzo (2019) Adaptive hierarchical priors for high-dimensional vector autoregressions | 0.737 | 3 | 3 | 67% |
| 5 | Ekambaram, V., A. Jati, N. H. Nguyen, P. Dayama, C. Reddy, W. M. Gif… (2024) Ttms: Fast multi-level tiny time mixers for improved zero-shot and few-shot forecasting of multivariate time series | 0.737 | 3 | 2 | 100% |
| 6 | Garza, A. and M. Mergenthaler-Canseco (2023) TimeGPT-1 | 0.737 | 3 | 2 | 100% |
| 7 | Rasul, K., A. Ashok, A. R. Williams, H. Ghonia, R. Bhagwatkar, A. Kh… (2024) Lag-llama: Towards foundation models for probabilistic time series forecasting | 0.737 | 3 | 2 | 100% |
| 8 | Woo, G., C. Liu, A. Kumar, C. Xiong, S. Savarese, and D. Sahoo (2024) Unified training of universal time series forecasting transformers | 0.737 | 3 | 2 | 100% |
| 9 | Carriero, A., T. E. Clark, and M. Marcellino (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors self | 0.644 | 2 | 2 | 100% |
| 10 | Carriero, A., T. E. Clark, and M. Marcellino (2016) Common drifting volatility in large Bayesian VARs self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 63 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.