In this paper, we add to the literature on the assessment of how well RBC simulated data reproduce the dynamic features of historical data. In particular, we evaluate a variety of new Keynesian DSGE models, including the standard sticky price model discussed in Calvo (1983), the sticky price with dynamic indexation model discussed in Christiano, Eichenbaum and Evans (2001), Smets and Wouters (2003), and Del Negro and Schorfheide (2005), and the sticky information model of Mankiw and Reis (2002). We carry out our evaluation by using standard impulse response and correlation measures and via use of a distribution based approach for comparing all of our (possibly) misspecified DSGE models via direct comparison of simulated inflation and output gap values with corresponding historical values. In this sense, our analysis can be thought of as an empirical model selection exercise. In addition, and given that one of our objectives is to choose the model which yields simulation distributions that are closest to the historical record, our analysis can be viewed as a type of predictive density model selection, where the best simulated distributions can be used as predictive densities whenever the starting values for the simulations correspond to those actual historical values which are most recently available. Some important precedents to our approach to accuracy assessment include DeJong, Ingram, and Whiteman (1996) and Geweke (1999a,b). One of our main findings is that for a standard level of stickiness (i.e. annual price or information adjustment), the sticky price model with indexation dominates other models. However, when models are calibrated using the lower level of information and price stickiness, there is much less to choose from between the models.
Empirical distribution model selection sticky information sticky price