Optimization-based macroeconomic models, and, in particular, dynamic stochastic general equilibrium (DSGE) setups, are popular nowadays for analyzing a multitude of macroeconomic questions such as the effects of monetary policy. But as models of this sort become increasingly complex, featuring many types of markets, various rigidities, and different non-linearities, the decision of whether to use a limited or full information (LI or FI) approach for estimation becomes a central question for model developers. Indeed, there appears to be a conflict in the conclusions of available published studies based on one or the other method. A striking example is the ongoing debate in the sizeable empirical literature with regard to the importance of the forward-looking component of the New Keynesian Phillips Curve (NKPC) equation. Recent contributions to this discussion include Gal?, Gertler, and Lopez-Salido (2005) that uses LI methods, and Linde (2005) that uses FI methods, and which report opposite outcomes with respect to the forward-looking nature of the curve.
The LI/FI trade-off is an enduring econometric problem, often presented as one of weighing specification bias versus efficiency, but there are also other concerns. In particular, advances in econometrics regarding weak-instruments and weak-identification have revealed that the latter plague LI and FI methods equally, thus presenting a set of new challenges for applied researchers.