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Eugen Tereanu

22 September 2021
RESEARCH BULLETIN - No. 87.3
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Abstract
Many countries have implemented macroprudential policies. The aims are twofold: first, to render the financial system more resilient to shocks and, second, to prevent booms and busts in the financial system in response to economic cycles. This article provides theoretical and empirical evidence which shows the positive impact that these measures have on financial stability, as well as the gains in economic growth derived from a stronger financial system.
JEL Code
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
28 May 2021
WORKING PAPER SERIES - No. 2559
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Abstract
Since the global financial crises, many countries have implemented macroprudential policies with the aim to render the financial system more resilient to shocks and limit the procyclicality of the financial system. We present theoretical and empirical evidence on the effectiveness of macroprudential policy, on both, financial stability and economic growth focussing on capital measures and borrower-based measures.
JEL Code
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
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Discussion papers
27 March 2019
MACROPRUDENTIAL BULLETIN - ARTICLE - No. 7
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Abstract
This article presents the ECB framework for assessing financial stability risks stemming from residential real estate markets and for designing macroprudential policy responses. It reviews recent developments in residential real estate markets and policy initiatives to address risks.
JEL Code
G01 : Financial Economics→General→Financial Crises
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
R30 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Real Estate Markets, Spatial Production Analysis, and Firm Location→General
28 February 2018
WORKING PAPER SERIES - No. 2134
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Abstract
We bring together the spatial and global vector autoregressive (GVAR) classes of econometric models by providing a detailed methodological review of where they meet in terms of structure, interpretation, and estimation methods. We discuss the structure of cross-section connectivity (weight) matrices used by these models and its implications for estimation. Primarily motivated by the continuously expanding literature on spillovers, we define a broad and measurable concept of spillovers. We formalize it analytically through the indirect effects used in the spatial literature and impulse responses used in the GVAR literature. Finally, we propose a practical step-by-step approach for applied researchers who need to account for the existence and strength of cross-sectional dependence in the data. This approach aims to support the selection of the appropriate modeling and estimation method and of choices that represent empirical spillovers in a clear and interpretable form.
JEL Code
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C38 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Classification Methods, Cluster Analysis, Principal Components, Factor Models
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation