Assessing Structural VARs

Christiano, Eichenbaum, and Vigfusson ask whether structural VAR (SVAR) methods reliably recover the dynamic response of hours worked to an identified shock, by generating artificial data from a series of estimated real-business-cycle (RBC) models and from the richer monetary DSGE model of Altig, Christiano, Eichenbaum, and Linde (2005) (ACEL), applying SVAR procedures to the artificial data, and checking the sampling properties (bias, confidence-interval coverage) of the resulting impulse-response estimates against the model’s true response. They find that, so long as the variance in hours explained by a given shock exceeds a “remarkably low” 1 percent, SVAR-based estimates of the response of hours to that shock have good sampling properties – confidence intervals accurately reflect true sampling uncertainty – regardless of whether identification uses short-run or long-run restrictions, though long-run-restriction confidence intervals are substantially wider. Short-run-restriction SVARs perform well even when technology shocks explain as little as 0.5% of the variance in hours. The authors trace the (occasional) bias in long-run-identified estimates to the difficulty of accurately estimating the sum of VAR coefficients with a finite-lag VAR when the true process is infinite-ordered, and propose and validate a modified estimator – based on a nonparametric estimate of the zero-frequency spectral density – that largely eliminates this bias in their examples. The paper’s conclusions about the overall reliability of long-run-identified SVARs differ sharply from the contemporaneous critique in Chari, Kehoe, and McGrattan (2008), which the authors attribute mainly to different parameter estimates of the relative importance of technology versus non-technology shocks, not to a disagreement about SVAR methodology itself.

Robert Vigfusson | Macro Paper Warehouse

Assessing Structural VARs