Impulse response analysis in conditional quantile models with an application to monetary policy

This paper presents a new method to analyse the effect of shocks on time series using a quantile impulse response function (QIRF). While conventional impulse response analysis is restricted to evaluation using the conditional mean function, here, we propose an alternative impulse response analysis that traces the effect of economic shocks on the conditional quantile function. By changing the quantile index over the unit interval, it is possible to measure the effects of shocks on the entire conditional distribution of a variable of interest in our framework. Therefore, we can observe the complete distributional consequences of policy interventions, especially at the upper and lower tails of the distribution as well as the mean. Using the new approach, it becomes possible to evaluate two distinct features (called "distributional effects"): (i) a change in the dispersion of the conditional distribution of interest is changed after a shock, and (ii) a change in the degree of skewness of the conditional distribution caused by a policy intervention. None of these features can be observed in the conventional impulse response analysis exclusively based on the conditional mean function. In addition to proposing the QIRF, our second contribution is to present a new way to jointly estimate a system of multiple quantile functions. Our proposal system quantile estimator is obtained by extending the result of Jun and Pinkse (2009) to the time series context. We illustrate the QIFR on a VAR model in a manner similar to Romer and Romer (2004) in order to assess the impact of a monetary policy shock on the US economy.