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  1. Essays in quantitative macroeconomics
    Author: Kase, Hanno
    Published: 21 December 2021
    Publisher:  European University Institute, Department of Economics, Florence

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    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Source: Union catalogues
    Language: English
    Media type: Dissertation
    Format: Online
    Other identifier:
    hdl: 1814/73515
    Subjects: Neuronale Netze; Makroökonomisches Modell; Neoklassische Synthese; Theorie
    Scope: 1 Online-Ressource (circa 94 Seiten), Illustrationen
    Notes:

    Dissertation, European University Institute, 2021
  2. Estimating nonlinear heterogeneous agents models with neural networks
    Author: Kase, Hanno; Melosi, Leonardo; Rottner, Matthias
    Published: [2022]
    Publisher:  Federal Reserve Bank of Chicago, [Chicago, Illinois]

    Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or... more

    Access:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 244
    Inter-library loan:
    No inter-library loan

     

    Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or assuming that all agents are identical. While relaxing these assumptions is well-known to give rise to complicated curse-of-dimensionality problems, it is often unclear how seriously these simplifications distort the dynamics and predictions of the model. We leverage the recent advancements in machine learning to develop a solution and estimation method based on neural networks that does not require these strong assumptions. We apply our method to a nonlinear Heterogeneous Agents New Keynesian (HANK) model with a zero lower bound (ZLB) constraint for the nominal interest rate to show that the method is much more efficient than existing global solution methods and that the estimation converges to the true parameter values. Further, this application sheds light on how effectively our method is capable to simultaneously deal with a large number of state variables and parameters, nonlinear dynamics, heterogeneity as well as aggregate uncertainty.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/267977
    Series: [Working paper] / Federal Reserve Bank of Chicago ; WP 2022, 26 (June 14, 2022)
    Subjects: Machine learning; neural networks; Bayesian estimation; global solution; heterogeneous agents; nonlinearities; aggregate uncertainty; HANK model; zero lower bound
    Scope: 1 Online-Ressource (circa 55 Seiten), Illustrationen
  3. Estimating nonlinear heterogeneous agents models with neural networks
    Author: Kase, Hanno; Melosi, Leonardo; Rottner, Matthias
    Published: 15 June 2022
    Publisher:  Centre for Economic Policy Research, London

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    Verlag (lizenzpflichtig)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    LZ 161
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    Location:
    Universitätsbibliothek Mannheim
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Array ; DP17391
    Subjects: Machine Learning; neural networks; Bayesian estimation; Global solution; Heterogeneous Agents; Nonlinearities; Aggregate uncertainty; HANK model; zero lower bound
    Scope: 1 Online-Ressource (circa 57 Seiten), Illustrationen