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Journal Article
Artificial Intelligence and Inflation Forecasts
Faria-e-Castro, Miguel; Leibovici, Fernando (2024-11-29)
We explore the ability of large language models (LLMs) to produce in-sample conditional inflation forecasts during the 2019–23 period. We use a leading LLM (Google AI’s PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years and at almost all horizons. LLM forecasts exhibit slower reversion to the 2 percent inflation anchor.
Review , Volume 106 , Issue 12 , Pages 14 pages

Working Paper
Explaining Machine Learning by Bootstrapping Partial Marginal Effects and Shapley Values
Cook, Thomas R.; Modig, Zach; Palmer, Nathan M. (2024-09-20)
Machine learning and artificial intelligence are often described as “black boxes.” Traditional linear regression is interpreted through its marginal relationships as captured by regression coefficients. We show that the same marginal relationship can be described rigorously for any machine learning model by calculating the slope of the partial dependence functions, which we call the partial marginal effect (PME). We prove that the PME of OLS is analytically equivalent to the OLS regression coefficient. Bootstrapping provides standard errors and confidence intervals around the point ...
Finance and Economics Discussion Series , Paper 2024-075

Working Paper
The Anatomy of Out-of-Sample Forecasting Accuracy
Rapach, David E.; Borup, Daniel; Goulet Coulombe, Philippe; Montes Schütte, Erik Christian; Schwenk-Nebbe, Sander (2022-11-07)
We develop metrics based on Shapley values for interpreting time-series forecasting models, including“black-box” models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics, iShapley-VI and oShapley-VI, measure the importance of individual predictors in fitted models for explaining the in-sample and out-of-sample predicted target values, respectively. The third metric is the performance-based Shapley value (PBSV), our main methodological contribution. PBSV measures the ...
FRB Atlanta Working Paper , Paper 2022-16

Working Paper
Machine Learning, the Treasury Yield Curve and Recession Forecasting
Puglia, Michael; Tucker, Adam (2020-05-20)
We use machine learning methods to examine the power of Treasury term spreads and other financial market and macroeconomic variables to forecast US recessions, vis-à-vis probit regression. In particular we propose a novel strategy for conducting cross-validation on classifiers trained with macro/financial panel data of low frequency and compare the results to those obtained from standard k-folds cross-validation. Consistent with the existing literature we find that, in the time series setting, forecast accuracy estimates derived from k-folds are biased optimistically, and cross-validation ...
Finance and Economics Discussion Series , Paper 2020-038

Working Paper
Evaluating Local Language Models: An Application to Bank Earnings Calls
Cook, Thomas R.; McAdam, Peter; Kazinnik, Sophia; Lundgaard Hansen, Anne (2023-11-06)
This study evaluates the performance of local large language models (LLMs) in interpreting financial texts, compared with closed-source, cloud-based models. We first introduce new benchmarking tasks for assessing LLM performance in analyzing financial and economic texts and explore the refinements needed to improve its performance. Our benchmarking results suggest local LLMs are a viable tool for general natural language processing analysis of these texts. We then leverage local LLMs to analyze the tone and substance of bank earnings calls in the post-pandemic era, including calls conducted ...
Research Working Paper , Paper RWP 23-12

Working Paper
Integrating Prediction and Attribution to Classify News
Rayl, Nelson P.; Sinha, Nitish R. (2022-07-01)
Recent modeling developments have created tradeoffs between attribution-based models, models that rely on causal relationships, and â€œpure prediction modelsâ€ such as neural networks. While forecasters have historically favored one technology or the other based on comfort or loyalty to a particular paradigm, in domains with many observations and predictors such as textual analysis, the tradeoffs between attribution and prediction have become too large to ignore. We document these tradeoffs in the context of relabeling 27 million Thomson Reuters news articles published between 1996 ...
Finance and Economics Discussion Series , Paper 2022-042

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