Fully Bayesian Estimation of Temporal Decay in Ordinal Relational Event Models

Relational event models (REMs) can infer the generative properties of longitudinally observed social networks with instantaneous edges. They assume conditional independence of edges given sufficient network statistics formed over the past event sequence. A popular specification in REMs is to subject these statistics to exponential temporal decay with a fixed half-life parameter to attribute higher importance to more recent edge events in the formation of network statistics. Assuming a fixed half-life parameter may cause biased estimates and obfuscates the temporal horizon over which network effects operate in empirical social systems. These limitations are addressed by proposing fully Bayesian estimation of REMs and designating the half-life parameter as an estimable quantity. A “pre-computation” strategy is devised to speed up calculations for practical feasibility of the sampling procedure. The approach is adapted to discourse network analysis, which models political actors’ statements about their preferred policy beliefs as dynamic networks. An application to the policy debate on reforming the German public pension system illustrates how temporal decay for inertia, actor activity, belief popularity, and actor homophily can be estimated alongside the main coefficients. Convergence diagnostics and an illustration of bias correction relative to fixed parameters are provided, stabilisation using hyper-parameters and computational complexity are discussed, and the approach is extended to include both Breslow’s and Efron’s methods for breaking ties in the event sequence.