Predictability Tests Robust against Parameter Instability
Christis Katsouris

TL;DR
This paper develops robust Wald-type tests for predictability and structural breaks in models with nonstationary predictors, providing new asymptotic theory and practical bootstrap methods, with applications to stock market predictability.
Contribution
It introduces analytical tractable asymptotic distributions for Wald tests using IVX estimators under nonstationarity, enhancing predictability testing with structural break considerations.
Findings
IVX-based tests filter out predictor persistence under certain conditions
Monte Carlo simulations show improved finite-sample performance
Application to US stock market predictability demonstrates practical utility
Abstract
We consider Wald type statistics designed for joint predictability and structural break testing based on the instrumentation method of Phillips and Magdalinos (2009). We show that under the assumption of nonstationary predictors: (i) the tests based on the OLS estimators converge to a nonstandard limiting distribution which depends on the nuisance coefficient of persistence; and (ii) the tests based on the IVX estimators can filter out the persistence under certain parameter restrictions due to the supremum functional. These results contribute to the literature of joint predictability and parameter instability testing by providing analytical tractable asymptotic theory when taking into account nonstationary regressors. We compare the finite-sample size and power performance of the Wald tests under both estimators via extensive Monte Carlo experiments. Critical values are computed using…
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Taxonomy
TopicsMonetary Policy and Economic Impact · Forecasting Techniques and Applications · Financial Risk and Volatility Modeling
