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Assessing the shape of the distribution of interest rates: lessons from French individual data

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  • Lacroix, R.
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    Abstract

    Estimating mixture models still raises numerous questions, both theoretical and empirical. However, this class of model appears quite powerful for a parcimonious modelization of ill-behaved distribution, as it is the case with loans rate vis-à-vis the private sector collected by the Banque de France from a panel of French credit institutions. Indeed, the heterogeneity of the credit market is a well-established fact which translate into high variability of interest rates at the micro level. Thus, we provide a detailed analysis of 11 categories of loans to non-financial corporations and households, and compare various procedures for the estimation of mixture models with a large number of components. The results allow us to identify modes in the distributions of interest rates and to clarify the nature of heterogeneity in the data in relation with the specialization of some part of the banking sector on particular instruments. Lastly, our methodology allows us to quantify the effects of the usury law on the upper side of the distribution, and to propose a preliminary estimate of eviction rates resulting from this regulation.

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    Bibliographic Info

    Paper provided by Banque de France in its series Working papers with number 206.

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    Length: 75 pages
    Date of creation: 2008
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    Handle: RePEc:bfr:banfra:206

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    Postal: Banque de France 31 Rue Croix des Petits Champs LABOLOG - 49-1404 75049 PARIS
    Web page: http://www.banque-france.fr/
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    Related research

    Keywords: Mixture Model ; Usury Law ; Credit Market.;

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    1. Andrews, Donald W K, 2001. "Testing When a Parameter Is on the Boundary of the Maintained Hypothesis," Econometrica, Econometric Society, vol. 69(3), pages 683-734, May.
    2. Peter C.B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Cowles Foundation Discussion Papers 1222, Cowles Foundation for Research in Economics, Yale University.
    3. Donald W. K. Andrews, 1999. "Estimation When a Parameter Is on a Boundary," Econometrica, Econometric Society, vol. 67(6), pages 1341-1384, November.
    4. Hansen, B.E., 1991. "Inference when a Nuisance Parameter is Not Identified Under the Null Hypothesis," RCER Working Papers 296, University of Rochester - Center for Economic Research (RCER).
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