Hausman & Woodward present an argument for the Causal Markov Condition (CMC) on the basis of a principle they dub ‘modularity’ ([1999, 2004]). I show that the conclusion of their argument is not in fact the CMC but a substantially weaker proposition. In addition, I show that their argument is invalid and trace this invalidity to two features of modularity, namely, that it is stated in terms of ...

This paper considers the problem of testing for cross section independence in limited dependent variable panel data models. It derives a Lagrangian multiplier (LM) test and shows that in terms of generalized residuals of Gourieroux, Monfort, Renault and Trognon (1987) it reduces to the LM test of Breusch and Pagan (1980). Due to the tendency of the LM test to over-reject in panels with large N ...

A test of adverse events and strategic timing theories can be conducted by determining whether some relevant financial decision variables, such as financial benefit from filing for bankruptcy, or debt discharged in bankruptcy are endogenous with the bankruptcy decision or not. For the strategic timing theory such decisions are endogenous, while for the adverse events theory they are not. Hausma...

Daniel Hausman and James Woodward claim to prove that the causal Markov condition, so important to Bayes-nets methods for causal inference, is the ‘flip side’ of an important metaphysical fact about causation—that causes can be used to manipulate their effects. This paper disagrees. First, the premise of their proof does not demand that causes can be used to manipulate their effects but rather ...

This paper proposes a variant of the classical HAUSMAN specifi cation test commonly employed to decide whether the estimation of a random-eff ects model is a viable alternative to estimating fi xed eff ects. Whereas the classical test probes the equality of fi xedand random eff ects, the proposed variant focuses on the equality of between-groups and fi xed-eff ects coeffi cients. While both tes...