Automatic Inference of the Contemporaneous Causal Order of a System of Equations

Abstract

When Stephen Perez and I first began our Monte Carlo studies of the efficacy of general-to-specific search methodologies in 1995, we were keenly aware of our limited ability to capture the tacit knowledge of the skilled time-series econometrician operating in the LSE tradition (Hoover and Perez 1999a, b). Econometrics, we believed, was an art, and our algorithm was not intended to replace the artist. David Hendry and Hans-Martin Krolzig have demonstrated that PCGets, an automatic model selection algorithm that implements general-to-specific search procedures, can be successfully applied to the individual equations of vector autoregressions (VARs), provided that the contemporaneous causal order is known so that the covariance matrix of the VAR is diagonal. Graph-theoretic causal-search algorithms provide a means of determining the contemporaneous causal order of a VAR needed before general-to-specific search procedures can be applied. This paper explains the basis for those algorithms with special reference to VARs.