Estimating Monetary Policy Reaction Functions Using Quantile Regressions

Monetary policy rule parameters are usually estimated at the mean of the interest rate distribution conditional

on inflation and an output gap. This is an incomplete description of monetary policy reactions

when the parameters are not uniform over the conditional distribution of the interest rate. I use quantile

regressions to estimate parameters over the whole conditional distribution of the federal funds rate. Inverse

quantile regressions are applied to deal with endogeneity. Real-time data of inflation forecasts and the

output gap are used. I find significant and systematic variations of parameters over the conditional interest

rate distribution. Testing for structural changes in regression quantiles shows that these parameter variations

cannot be explained by preference shifts of the Fed. Asymmetric interest rate responses can rather be

related to expansions and recessions and are consistent with a recession avoidance preference of the Fed

during the Volcker-Greenspan era.