2007/02/01 by Valentina Corradi, Norman R. Swanson · 113 citations
Economics, Econometrics and Finance · Mathematics · #Artificial intelligence #Computer science #Context (archaeology) #Econometrics #Frequentist inference #Inference #Italy: Economic History and Contemporary Issues #Market Dynamics and Volatility #Mathematics #Monetary Policy and Economic Impact #Monte Carlo method #Nonparametric statistics #Parametric statistics #Predictive inference #Sample (material) #Statistics
paper · pdf · doi:10.1111/j.1468-2354.2007.00418.x
published in International Economic Review 48(1), 67-109 (Wiley)
openalex publication_date 2007/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for out‐of‐sample nonlinear Granger causality, and in the other we outline a test for selecting among multiple alternative forecasting models, all of which are possibly misspecified. In a Monte Carlo investigation, we compare the finite sample properties of our block bootstrap procedures with the parametric bootstrap due to Kilian ( Journal of Applied Econometrics 14 (1999), 491–510), within the context of encompassing and predictive accuracy tests. In the empirical illustration, it is found that unemployment has nonlinear marginal predictive content for inflation.