PreprintCited by 9Stochastic processes and financial applicationsEconomic theories and modelsAdaptive Dynamic Programming Control

Macro, Finance, and Macro Finance: Solving Nonlinear Models in Continuous Time with Machine Learning

Open access
Duarte, Victor·2017·SSRN Electronic Journal

Integrity signals

Registry

OpenAlex · 10.2139/ssrn.3012602

Cites

118 of 118 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • ML has the potential to help with sensitivity and uncertainty analysis, with finding numerical solutions for computationally expensive submodels, and assessing the validity of models.

    “ML also has the potential to help with sensitivity and uncertainty analysis [386], with finding numerical solutions for computational expensive submodels [206,714], and assessing the validity of the models [556].”

    From Tackling climate change with machine learning (2023) · cites this paper as [206]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Duarte, V.
Affiliations
University of Illinois Urbana-Champaign (US)
Corresponding author
Duarte, Victor
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2017
ISSN
1556-5068 (ISSN-L)
OpenAlex
W2751249614
Cited by
9
References
115
Citations by year
2022: 2 · 2021: 2 · 2020: 2 · 2019: 1 · 2018: 2
Type
posted content
Language
en
Open access
Locations
SSRN Electronic Journal (publishedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Finance › Stochastic processes and financial applications
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic theories and models
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Adaptive Dynamic Programming Control
Keywords
macro-finance, supervised learning, Hamilton-Jacobi-Bellman equation, neoclassical growth model, value function, productivity function, open-source library

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review