Parallel Extended Path Method for Solving Perfect Foresight Models

N. B. Melnikov (), A. P. Gruzdev, M. G. Dalton, Matthias Weitzel and B. C. O’Neill
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N. B. Melnikov: Lomonosov Moscow State University
A. P. Gruzdev: Lomonosov Moscow State University
M. G. Dalton: National Oceanic and Atmospheric Administration
B. C. O’Neill: University of Denver

Computational Economics, 2021, vol. 58, issue 2, No 13, 517-534

Abstract: Abstract We parallelize the extended path method for solving rational expectations models, and apply it to compute perfect foresight competitive equilibrium for the global economy with multiple goods, regions, industries, and households. At each iteration, first intertemporal variables are updated, then equations for intra-temporal variables are solved in parallel. We compare serial, and parallel, versions of the extended path method in high-performance computing environments based on scenarios with long time horizons that include future populations, economic growth, energy use, and carbon dioxide emissions. Relative to the serial version, the speedup factor for the parallel extended path method grows almost linearly up to about 30 times with 18 cores, and computing times reduced from over 10 h for the serial version down to about 20 min for the parallel version.

Keywords: Perfect foresight; Intertemporal general equilibrium; Economic growth; Iterative methods; Parallel computing; Energy economics; Climate impacts (search for similar items in EconPapers)
JEL-codes: C63 D58 J11 O13 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10614-020-10044-y

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