Working Paper: CEPR ID: DP3701
Authors: Domenico Giannone; Lucrezia Reichlin; Luca Sala
Abstract: Equilibrium business cycle models have typically less shocks than variables. As pointed out by Altug, 1989, and Sargent, 1989, if variables are measured with error, this characteristic implies that the model solution for measured variables has a factor structure. This Paper compares estimation performance for the impulse response coefficients based on a VAR approximation to this class of models and an estimation method that explicitly takes into account the restrictions implied by the factor structure. Bias and mean squared error for both factor based and VAR based estimates of impulse response functions are quantified using, as a data generating process, a calibrated standard equilibrium business cycle model. We show that, at short horizons, VAR estimates of impulse response functions are less accurate than factor estimates while the two methods perform similarly at medium and long run horizons.
Keywords: Dynamic factor models; Equilibrium business cycle models; Identification; Structural VARs
JEL Codes: C33; C52; E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| measurement error (C20) | VAR estimates (C29) |
| measurement error (C20) | factor estimates (C51) |
| VAR estimates (C29) | accuracy of impulse response estimates (C51) |
| factor estimates (C51) | accuracy of impulse response estimates (C51) |
| measurement error (C20) | reduced stochastic rank (C69) |
| measurement error (C20) | bias in VAR estimates (C32) |