Quantitative Macroeconomics: Lessons Learned from Fourteen Replications

Abstract

I replicate all tables and figures from fourteen papers in Quantitative Macroeconomics, with an emphasis on incomplete market heterogeneous agent models. I report three main findings: (i) all (non-welfare related) major findings of the papers replicate, (ii) welfare findings based on linear approximation methods—1st-order perturbation, linear and log-linearization around steady-state, and linear-quadratic methods—should be treated as quantitatively suspect, (iii) decisions around methods for discretizing exogenous shocks have a large and unappreciated influence on results and should be prominently discussed in papers. While some smaller aspects of the papers do not replicate exactly, rather than nitpick in the body of this paper I instead describe some lessons learnt that may be useful for practitioners working with Quantitative Macroeconomic models. The replications use global methods allowing for non-linearities and I argue that these are important and need to be more widely used. I provide a checklist that researchers can use when trying to check that their work will be more easily reproducible. Matlab codes implementing the replications using the VFI Toolkit are provided, and full results of all replications are given in the online appendix. I conclude with three core points for best practice: (i) codes be made directly available (e.g., on github, not only ’on request’, and not just inside a zip file), (ii) report not just baseline parameters but also hyperparameters, equilibrium values, non-baseline parameters and initial conditions, and (iii) replication means rewriting codes from scratch, not just re-running available codes.

Going faster to see further: GPU-accelerated value iteration and simulation for perishable inventory control using JAX

“…2 Related work Jóhannsson (2009) demonstrated that value iteration could be effectively run in parallel on a GPU soon after the introduction of CUDA. Subsequent research has evaluated the performance of GPU-accelerated value iteration on problems from economics and finance (Aamer et al 2020;Aldrich et al 2011;Duarte et al 2020;Kirkby 2017;Kirkby 2022) and route-finding and navigation (Chen and Lu 2013;Constantinescu et al 2020;Inamoto et al 2011;Ruiz and Hernández 2015). We have only identified a single study that applied this approach to an inventory control problem: Ortega et al (2019) implemented a custom value iteration algorithm in CUDA to find replenishment policies for a subset of perishable inventory problems originally described by Hendrix et al (2019).…”