Towards a HANK Model for Canada: Estimating a Canadian Income Process

I study individual earnings dynamics using panel data on Canadian workers. I first show that, similar to US findings, the distribution of Canadian income growth is leptokurtic. To generate such high kurtosis, I use a common continuous-time specification of the individual earnings as a stochastic process with a random (Poisson) arrival of normally distributed jumps. The fitted earnings process matches the eight targeted moments well. The estimated parameter values are consistent with the existence of both transitory and persistent components in earnings. On the methodological side, I show how the estimation process can be accelerated significantly by parallelizing Monte Carlo simulations on graphical processing units with massive savings in computational time. My estimates represent a key first step in developing quantitatively realistic Heterogeneous Agent New Keynesian (HANK) models for the Canadian economy. HANK models are important tools for understanding consumption behaviour and analyzing the transmission mechanism for monetary policy. The estimated process in this paper may prove useful in other contexts where an empirically realistic representation of household earnings dynamics is vital.