Research Details

Abstract

This paper proposes a consistent inference procedure for predictive regressions in economic systems governed by fractionally integrated vector autoregressive dynamics that may be subject to low-frequency contamination such as structural, or Markov-switching, breaks, outliers and deterministic trends. Specifically, we modify the local spectrum (LCM) inference procedure (Andersen & Varneskov (2019)), making it robust towards such features by establishing new results for the first-stage filtering as well as the second-stage trimming. The modified LCM estimator and tests are asymptotically normal and chi^2, respectively, despite the variables having different integration orders and being low-frequency contaminated. As bi-products of our analysis, we provide a modified exact local Whittle estimator of the integration order and a modified test for cointegration rank, which are both robust to contamination. Furthermore, we provide consistent estimators of the coherence between (unknown) low-frequency contaminants. The validity of the estimators and tests are examined in a simulation study. Finally, we use the new framework to dissect the dynamic properties and dependencies in popular first-order VAR systems in macro finance.

Type

Article

Author(s)

Torben Andersen, Rasmus Tangsgaard Varneskov

Date Published

2022

Citations

Andersen, Torben, and Rasmus Tangsgaard Varneskov. 2022. Testing for Parameter Instability and Structural Change in Persistent Predictive Regressions. Journal of Econometrics.: 361-386.