Sieve Bootstrap Approach to Robust Term Premia Analysis

Robust inference in bond predictive regressions faces challenges due to strong time-series per-sistence and unknown cross-sectional factor structures in the bond yield vector. These diffi-culties are particularly pronounced in analyzing the spanning hypothesis, which tests whether factors beyond the first three principal components (PCs)—level, slope, and curvature—improve bond return predictability. To address this, we develop a novel nonparametric sieve bootstrap approach for multivariate bond yield data with different maturities. Our method provides accurate size and improved power performance in bond predictive regression, com-pared to existing bootstrap inference procedures for the spanning hypothesis. Revisiting Cochrane and Piazzesi (2005)’s return-forecasting factor, we find strong evidence of its pre-dictive power beyond the first three PCs for bond excess returns in most sample periods after the 1960s. However, we find that these predictive gains significantly decline when the sample period extends to include recent years after 2019.