Cross-lags and the unbiased estimation of life-history and demographic parameters.

ABSTRACT: Biological processes exhibit complex temporal dependencies due to the sequential nature of allocation decisions in organisms' life cycles, feedback loops and two-way causality. Consequently, longitudinal data often contain cross-lags: the predictor variable depends on the response variable of the previous time step. Although statisticians have warned that regression models that ignore such covariate endogeneity in time series are likely to be inappropriate, this has received relatively little attention in biology. Furthermore, the resulting degree of estimation bias remains largely unexplored. We use a graphical model and numerical simulations to understand why and how regression models that ignore cross-lags can be biased, and how this bias depends on the length and number of time series.

SUBMITTER: van de Pol M 

PROVIDER: S-EPMC9290935 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature