Central Bank Research Hub - JEL classification C46: Specific Distributions
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Latest research hub papers with the JEL classification:C46en23Jun/Marginal Distributions of Random Vectors Generated by Affine Transformations of Independent Two-Piece Normal Variables
http://www.bportugal.pt/en-US/BdP%20Publications%20Research/wp201013.pdf
Bank of Portugal Working papers by Maximiano PinheiroMarginal Distributions of Random Vectors Generated by Affine Transformations of Independent Two-Piece Normal Variables2010-06-23T12:46:00ZMarginal probability density and cumulative distribution functions are presented for multidimensional variables defined by non-singular affine transformations of vectors of independent two-piece normal variables, the most important subclass of Ferreira and Steel’s general multivariate skewed distributions. The marginal functions are obtained by first expressing the joint density as a mixture of Arellano-Valle and Azzalini’s unified skew-normal densities and then using the property of closure under marginalization of the latter class.Marginal Distributions of Random Vectors Generated by Affine Transformations of Independent Two-Piece Normal Variables2010-06-23T12:46:00ZAbstracthttp://www.bportugal.pt/en-US/EstudosEconomicos/Publicacoes/Pages/BdPPublicationsResearchDetail.aspx?PublicationId=500Full texthttp://www.bportugal.pt/en-US/BdP%20Publications%20Research/wp201013.pdfMaximiano PinheiroMaximiano Pinheiro2010-06Bank of Portugal Working papersC4612Aug/Noncausal vector autoregression
http://www.bof.fi/NR/rdonlyres/80E06020-5B86-45D7-B4EF-6EEF49D85E2E/0/0918netti.pdf
Bank of Finland Discussion Papers by Markku Lanne – Pentti SaikkonenNoncausal vector autoregression2009-08-12T17:40:00ZIn this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and statistical inference. We argue that allowing for noncausality is of importance in empirical economic research, which currently uses only conventional causal VAR models. Indeed, if noncausality is incorrectly ignored, the use of a causal VAR model may yield suboptimal forecasts and misleading economic interpretations. This is emphasized in the paper by noting that noncausality is closely related to the notion of nonfundamentalness, under which structural economic shocks cannot be recovered from an estimated causal VAR model. As detecting nonfundamentalness is therefore of great importance, we propose a procedure for discriminating between causality and noncausality that can be seen as a test of nonfundamentalness. The methods are illustrated with applications to fiscal foresight and the term structure of interest rates.Noncausal vector autoregression2009-08-12T17:40:00ZAbstracthttp://www.bof.fi/en/julkaisut/tutkimukset/keskustelualoitteet/2009/dp2009_18.htmFull texthttp://www.bof.fi/NR/rdonlyres/80E06020-5B86-45D7-B4EF-6EEF49D85E2E/0/0918netti.pdfPentti SaikkonenMarkku LanneMarkku Lanne – Pentti Saikkonen2009-08-12Bank of Finland Discussion PapersC32C46C52E62G1206Mar/Effects of unobserved defaults on correlation between probability of default and loss given default on mortgage loans
http://www.bof.fi/NR/rdonlyres/4014D355-08B7-4ED5-B6A1-7EF66E99D950/0/0903netti.pdf
Bank of Finland Discussion Papers by Peter PalmroosEffects of unobserved defaults on correlation between probability of default and loss given default on mortgage loans2009-03-06T12:37:00ZThis paper demonstrates how the observed correlation between probability of default and loss given default depends on the fact that defaults in which collateral provides 100% recovery are not observed. Creditors see only the defaults of mortgagors who suffer from a fall in collateral value to less than the remaining loan principal. Consequently, the default data available to creditors amounts to a mere truncated sample from the underlying population of defaults. Correlation estimates based on such truncated samples are biased and differ substantially from estimates derived from representative non-truncated samples. Moreover, the observed correlation between default probability and loss given default is sensitive to the truncation point, which may explain the differences in correlation estimates found in the literature. This may also explain why correlation estimates seem to be specific to cycle phase.Effects of unobserved defaults on correlation between probability of default and loss given default on mortgage loans2009-03-06T12:37:00ZAbstracthttp://www.bof.fi/en/julkaisut/tutkimukset/keskustelualoitteet/2009/dp2009_03.htmFull texthttp://www.bof.fi/NR/rdonlyres/4014D355-08B7-4ED5-B6A1-7EF66E99D950/0/0903netti.pdfPeter PalmroosPeter Palmroos2009-01-19Bank of Finland Discussion PapersC46E32G21G28