Estimating HANK: Macro Time Series and Micro Moments


We show how to use sequential Monte Carlo methods to estimate a heterogeneous agent New Keynesian (HANK) model featuring both nominal and real rigidities. We demonstrate how the posterior distribution may be specified as the product of the standard macro time series likelihood and a prior enforcing several steady state distributional moments, including the average marginal propensity to consume (MPC) and fraction of agents with zero liquid wealth. Using this framework, we ask whether there exists a tension between fitting macroeconomic time series and distributional moments of interest, ultimately finding there is none. For instance, even after relaxing the prior, the posterior based solely on macro time series features an MPC well below one, broadly in line with existing micro evidence.

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Ph.D. Student in Economics and Statistics

I am an Economics Ph.D. candidate at MIT, and former Senior Research Analyst on the dynamic stochastic general equilibrium (DSGE) team in the Macroeconomic and Monetary Studies function at the NY Fed. Views expressed are my own.