Relatore: Prof. Giovanni Millo, DEAMS

Titolo: Specifying spatial effects in panel data: Robust vs. conditional tests

Abstract:

A key issue in spatial models is to appropriately specify the spatial effect. Robust LM tests have long been popular in spatial econometrics for discriminating between spatial lag and spatial error processes. Their application has recently been extended to spatial panels, a context where further issues arise the tests were not designed to address in the first place: individual heterogeneity and time persistence.

Through Monte Carlo simulation, we show that RLM tests become virtually useless as a specification device under substantial individual heterogeneity, regardless whether correlated or not. If the heterogeneity is correlated (fixed effects) then the tests are theoretically inappropriate. If it is not (random effects), their properties severely deteriorate.

Eliminating individual effects by time-demeaning the data greatly improves the situation, making them a viable, computationally simpler second-best alternative to the optimal ML-based tests. We therefore suggest to always demean the data, not only in the obvious case of testing fixed effects models.

Nevertheless, conditional tests are to be preferred unless the problem is forbiddingly ill-conditioned.