Numerical Methods for Macroeconomists

Jeremy Greenwood and Ricardo Marto

No 36, Economie d'Avant Garde Research Reports from Economie d'Avant Garde

Abstract: This primer will cover some of the numerical methods that are used in modern macroeconomics. You will learn how to: (1) solve nonlinear equations via bisection and Newton's method; (2) compute maximization problems via golden section search, discretization, and the particle swarm algorithm; (3) simulate difference equations using the extended path and multiple shooting algorithms; (4) differentiate and integrate functions numerically; (5) conduct Monte Carlo simulations by drawing random variables; (6) construct Markov chains; (7) interpolate functions and smooth data; (8) compute dynamic programming problems; (9) solve for policy functions using the Coleman, endogenous grid, and parameterized expectation algorithms; (10) study the Aiyagari heterogeneous agent model with and without aggregate uncertainty. This will be done while studying economic problems, such as the determination of labor supply, economic growth, and business cycle analysis. Calculus is an integral part of the primer and some elementary probability theory will be drawn upon. The MATLAB programming language will be used. It is time to move into the modern age and learn these techniques. Besides, using computers to solve economic models is fun. The primer is self contained so little prior knowledge is required. This is work in progress. Comments are welcome.

Keywords: Aiyagari model; calibration; Coleman algorithm; difference equations; dynamic programming; endogenous grid method; interpolating functions; linearization; Markov chains; maximization problems; Monte Carlo simulation; nonlinear equations; numerical differentiation and integration; parameterized expectations; random number generation (search for similar items in EconPapers)
JEL-codes: E10 E17 (search for similar items in EconPapers)
Date: 2022-06
New Economics Papers: this item is included in nep-cmp, nep-dge and nep-mac
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