Consistent Inference for Predictive Regressions in Persistent Economic Systems

Working Paper: NBER ID: w28568

Authors: Torben G. Andersen; Rasmus T. Varneskov

Abstract: We study standard predictive regressions in economic systems governed by persistent vector autoregressive dynamics for the state variables. In particular, all – or a subset – of the variables may be fractionally integrated, which induces a spurious regression problem. We propose a new inference and testing procedure – the Local speCtruM (LCM) approach – for joint significance of the regressors, that is robust against the variables having different integration orders and remains valid regardless of whether predictors are significant and if they induce cointegration. Specifically, the LCM procedure is based on fractional filtering and band-spectrum regression using a suitably selected set of frequency ordinates. Contrary to existing procedures, we establish a uniform Gaussian limit theory and a standard χ2-distributed test statistic. Using LCM inference and testing techniques, we explore predictive regressions for the realized return variation. Standard least squares inference indicates that popular financial and macroeconomic variables convey valuable information about future return volatility. In contrast, we find no significant evidence using our robust LCM procedure. If anything, our tests support a reverse chain of causality: rising financial volatility predates adverse innovations to macroeconomic variables. Simulations illustrate the relevance of the theoretical arguments for finite-sample inference.

Keywords: predictive regressions; fractional integration; volatility forecasting; local spectrum approach

JEL Codes: G12; G17

Causal Claims

Cause	Effect
financial volatility (G19)	adverse innovations to macroeconomic variables (E19)
elevated volatility (E32)	widening default spread (D39)
elevated volatility (E32)	drop in the price-earnings ratio (G19)
standard least squares inference (C20)	significant size distortions (F12)