Using the Sequence-Space Jacobian to Solve and Estimate Heterogeneous-Agent Models
Adrien Auclert (),
Matthew Rognlie () and
No 13890, CEPR Discussion Papers from C.E.P.R. Discussion Papers
We propose a general and highly efficient method for solving and estimating general equilibrium heterogeneous-agent models with aggregate shocks in discrete time. Our approach relies on the rapid computation and composition of sequence-space Jacobians-the derivatives of perfect-foresight equilibrium mappings between aggregate sequences around the steady state. We provide a fast algorithm for computing Jacobians for heterogeneous agents, a technique to substantially reduce dimensionality, a rapid procedure for likelihood-based estimation, a determinacy condition for the sequence space, and a method to solve nonlinear perfect-foresight transitions. We apply our methods to three canonical heterogeneous-agent models: a neoclassical model, a New Keynesian model with one asset, and a New Keynesian model with two assets.
Keywords: Computational Methods; General Equilibrium; Heterogeneous Agent; linearization (search for similar items in EconPapers)
JEL-codes: C63 E21 E32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-mac and nep-ore
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Working Paper: Using the Sequence-Space Jacobian to Solve and Estimate Heterogeneous-Agent Models (2019)
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