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The Anatomy of Out-of-sample Forecasting Accuracy

Daniel BorupPhilippe Goulet CoulombeDavid E. RapachErik Christian Montes SchütteSander Schwenk-Nebbe

出版	Federal Reserve Bank of Boston, 2022
URL	http://books.google.com.hk/books?id=xoKHzwEACAAJ&hl=&source=gbs_api

註釋

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.