Macroeconomic resilience in a DSGE model

We use the dynamic stochastic general equilibrium (DSGE) model of Altig et al. (2005) to analyse the resilience of an economy in the face of external shocks. The term resilience refers to the ability of an economy to prosper in the face of shocks. The Altig et al. model was chosen because it combined both demand and supply shocks and because various market rigidities/imperfections, which have the potential to affect resilience, are modelled. We consider the level of expected discounted utility to be the relevant measure of resilience. The effect of market rigidities, eg. wage and price stickiness, on the expected level of utility is minimal. The effect on utility is especially small when compared to the effect of market competition, because the latter has a direct effect on the level of output. This conclusion holds for the family of constant-relative-risk-aversion-over-consumption utility functions. A similar conclusion was drawn by Lucas (1987) regarding the costs of business cycles. We refer to the literature that followed Lucas for ideas for how a DSGE model might be adjusted to give a more meaningful analysis of resilience. We conclude that the Altig et al. DSGE model does not produce a relationship between rigidities and the level of output and, hence, does not capture the effect of inflexibility on utility that one observes colloquially.

Evaluating CPB's published GDP growth forecasts; a comparison with individual and pooled VAR based forecasts

We compare the accuracy of our published GDP growth forecasts from our large macro model, SAFFIER, to those produced by VAR based models using both classical and Bayesian estimation techniques. We employ a data driven methodology for selecting variables to include in our VAR models and we find that a randomly selected classical VAR model performs worse in most cases than the Bayesian equivalent, which performs worse than our published forecasts in most cases. However, when we pool forecasts across many VARs we can produce more accurate forecasts than we published. A review of the literature suggests that forecast accuracy is likely irrelevant for the non-forecasting activities the model is used for at CPB because they are fundamentally different activities.