Chapter 19 Inference and causality in economic time series models

This chapter is concerned with a particular formalization that has proved useful in empirical work, hence the juxtaposition of causality and inference. It also bears close relation to notions of strictly exogenous and predetermined variables, which have considerable operational significance in statistical inference, and to the concepts of causal orderings and reliability which are important in model construction in econometrics and engineering. Causality is defined in terms of predictability; it cannot be an acceptable definition of causation for most philosophers of science. The chapter focuses on the operational usefulness of the definition in the construction, estimation, and application of econometric models. It considers the logical relationships among WienerGranger causality, Simon's definition of causal ordering, the engineer's criterion of reliability, and the concept of structure. It also discusses that unidirectional causality from X to Y is not equivalent to the assertion that X is predetermined in a particular behavioral relationship whose parameters are to be estimated. It further focuses on parameterization problems, processes that are nonautoregressive or have deterministic components or are nonstationary, and inference about many variables.