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  1. The evolution of the response of credit spread variables to monetary policy shocks
    Author: Kim, Do-wan
    Published: 2024. 1
    Publisher:  Bank of Korea, Seoul, Korea

    Access:
    Verlag (kostenfrei)
    Verlag (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    VS 629
    Inter-library loan:
    No inter-library loan
    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: BOK working paper ; no. 2024, 1
    Subjects: Time-Varying Parameter VAR; Credit spreads; External Instrument; Monetary policy; Heteroscedasticity
    Scope: 1 Online-Ressource (circa 76 Seiten), Illustrationen
  2. Identifying demand elasticity via heteroscedasticity
    a panel GMM approach to estimation and inference
    Author: Brasch, Thomas von; Raknerud, Arvid; Vigtel, Trond C.
    Published: October 2024
    Publisher:  Statistics Norway, Research Department, Oslo

    This paper introduces a panel GMM framework for identifying and estimating demand elasticities via heteroscedasticity. While existing panel estimators address the simultaneity problem, the state-ofthe-art Feenstra/Soderbery (F/S) estimator suffers... more

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

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 619
    Inter-library loan:
    No inter-library loan

     

    This paper introduces a panel GMM framework for identifying and estimating demand elasticities via heteroscedasticity. While existing panel estimators address the simultaneity problem, the state-ofthe-art Feenstra/Soderbery (F/S) estimator suffers from inconsistency, inefficiency, and lacks a valid framework for inference. We develop a constrained GMM (C-GMM) estimator that is consistent and derive a uniform formula of its asymptotic standard error that is valid even at the boundary of the parameter space. A Monte Carlo study demonstrates the consistency of the C-GMM estimator and shows that it substantially reduces bias and root mean squared error compared to the F/S estimator. Unlike the F/S estimator, the C-GMM estimator maintains high coverage of confidence intervals across a wide range of sample sizes and parameter values, enabling more reliable inference.

     
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/305432
    Series: Discussion papers / Statistics Norway, Research Department ; 1015
    Subjects: Demand Elasticity; Panel Data; Heteroscedasticity; GMM; Constrained Estimation; Bagging
    Scope: 1 Online-Ressource (circa 48 Seiten), Illustrationen