Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/270459 
Year of Publication: 
2022
Series/Report no.: 
Working Paper No. 2022-16
Publisher: 
Federal Reserve Bank of Atlanta, Atlanta, GA
Abstract: 
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 contributions of individual predictors in fitted models to the out-of-sample loss and thereby anatomizes out-of-sample forecasting accuracy. In an empirical application forecasting US inflation, we find important discrepancies between individual predictor relevance according to the in-sample iShapley-VI and out-ofsample PBSV. We use simulations to analyze potential sources of the discrepancies, including overfitting, structural breaks, and evolving predictor volatilities.
Subjects: 
variable importance
out-of-sample performance
Shapley value
loss function
machine learning
inflation
JEL: 
C22
C45
C53
E37
G17
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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