Essays on theory and computation in economics
department or school: Arts, Faculty of
department or school: Vancouver School of Economics
data provider: DSpace
degree campus: UBCV
creator: Ebrahimi Kahou, Mahdi
issued: 2023-07-07T18:44:24Z
issued: 2023
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: The first chapter: I propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The curse of dimensionality is alleviated thanks to three techniques: (1) exploiting symmetry in the approximate law of motion and the policy function; (2) constructing a concentration of measure to calculate high-dimensional expectations using a single Monte Carlo draw from the distribution of idiosyncratic shocks; and (3) designing and training deep learning architectures that exploit symmetry and concentration of measure. As an application, I find a global solution of a multi-firm version of the classic Lucan and Prescott (1971) model of investment under uncertainty. First, I compare the solution against a linear-quadratic Gaussian version for benchmarking. Next, I solve the nonlinear version where no accurate or closed-form solution exists. Finally, I describe how this approach applies to a large class of models in economics. The second chapter: in the long run, we are all dead. Nonetheless, even when investigating short-run dynamics, models require boundary conditions on long-run, forward-looking behavior (e.g., transversality condition). In this chapter, in sequential setups, I show how deep learning approximations can automatically fulfill these conditions despite not directly calculating the steady state and balanced growth path. The main implication is that one can solve for transition dynamics with forward-looking agents, confident that long-run boundary conditions will implicitly discipline the short-run decisions, even converging towards the correct equilibria in cases with steady-state multiplicity. While this chapter analyzes benchmark models such as the neoclassical growth model, the results suggest deep learning may allow us to calculate accurate transition dynamics with high-dimensional state spaces, and without directly solving for long-…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/85208
note: Essays on Theory and Computation inEconomicsbyMahdi Ebrahimi KahouB.Sc., Sharif University of Technology, 2010M.Sc., University of Calgary, 2013A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Economics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)July 2023© Mahdi Ebrahimi Kahou, 2023The following individuals certify that they have read, and recommend to the Faculty of Graduateand Postdoctoral Studies for acceptance, the dissertation titled:Essays on Theory and Computation in Economicssubmitted by Mahdi Ebrahimi Kahou in partial fulfillment of the requirements forthe degree of Doctor of Philosophyin EconomicsExamining Committee:Jesse Perla, Associate Professor, Vancouver School of Economics, UBCSupervisorKyungchul (Kevin) Song, Professor, Vancouver School of Economics, UBCSupervisory Committee MemberPaul T. Schrimpf, Associate Professor, Vancouver School of Economics, UBCUniversity ExaminerDaniel McDonald, Associate Professor, Department of Statistics, UBCUniversity ExaminerAdditional Supervisory Committee Member:Hiroyuki Kasahara, Professor, Vancouver School of Economics, UBCSupervisory Committee MemberVadim Marmer, Professor, Vancouver School of Economics, UBCSupervisory Committee MemberiiAbstractThe first chapter: I propose a new method for solving high-dimensional dynamic programmingproblems and recursive competitive equilibria with a large (but finite) number of heterogeneousagents using deep learning. The curse of dimensionality is alleviated thanks to three techniques:(1) exploiting symmetry in the approximate law of motion and the policy function; (2) constructinga concentration of measure to calculate high-dimensional expectations using a single Monte Carlodraw from the distribution of idiosyncratic shocks; and (3) designing and training deep learningarchitectures that exploit symmetry and concentration of measure. As an application, I find aglobal solution o…
has type: Thesis/Dissertation
date issued: 2023-11
is shown at: 10.14288/1.0434173
language: eng
degree discipline: Economics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Perla, Jesse
type: Text
identifier URI: http://hdl.handle.net/2429/85208