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Journal Article
Consistent Local Spectrum Inference for Predictive Return Regressions
Econometric Theory
Author(s)
This paper studies the properties of predictive regressions for asset returns in
economic systems governed by persistent vector autoregressive dynamics. In particular,
we allow for the state variables to be fractionally integrated, potentially of
different orders, and for the returns to have a latent persistent conditional mean,
whose memory is difficult to estimate consistently by standard techniques in finite
samples. Moreover, the predictors may be endogenous and “imperfect.” In this
setting, we develop a consistent local spectrum (LCM) estimation procedure, that
delivers asymptotic Gaussian inference. Furthermore, we provide a new LCM-based
estimator of the conditional mean persistence, that leverages biased regression slopes
as well as new LCM-based tests for significance of (a subset of) the predictors,
which are valid even without estimating the return persistence. Simulations illustrate
the theoretical arguments. Finally, an empirical application to monthly S&P 500
return predictions provides evidence for a fractionally integrated conditional mean
component. Our new LCM procedure and tools indicate significant predictive power
for future returns stemming from key state variables such as the default spread and
treasury interest rates.
Date Published:
2022
Citations:
Andersen, Torben Gustav, Rasmus Varneskov. 2022. Consistent Local Spectrum Inference for Predictive Return Regressions. Econometric Theory.