Correlated Residuals in Lagged-Effects Models: What They (Do Not) Represent in the Case of a Continuous-Time Process

Abstract

The appeal of lagged-effects models, like the first-order vector autoregressive (VAR(1)) model, is the interpretation of the lagged coefficients in terms of predictive-and possibly causal-relationships between variables over time. While the focus in VAR(1) applications has traditionally been on the strength and sign of the lagged relationships, there has been a growing interest in the residual relationships (i.e., the correlations between the innovations) as well. In this article, we will investigate what residual correlations can and cannot signal, for both the discrete-time (DT) and continuous-time (CT) VAR(1) model, when inspecting a CT process. We will show that one should not take on a DT perspective when investigating a CT process: Correlated (i.e., non-zero) DT residuals can flag omitted common causes and effects at shorter intervals (which is well-known), but-when having a CT process-also effects at longer intervals. Furthermore, when inspecting a CT process, uncorrelated (i.e., zero) DT residuals do not imply that the variables have no effect on each other at other intervals, nor does it preclude the risk of having omitted common causes. Additionally, we will show that residual correlations in a CT model signal omitted causes for one or more of the observed variables. This may bias the estimation of lagged relationships, implying that the found predictive lagged relationships do not equal the underlying causal lagged relationships. Unfortunately, the CT residual correlations do not reflect the magnitude of the distortion.