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pro vyhledávání: '"Hoogerheide, Lennart F."'
Publikováno v:
In Journal of Econometrics 2007 139(1):154-180
Akademický článek
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Autor:
Ardia, David, Hoogerheide, Lennart F.
Publikováno v:
In Economics Letters May 2014 123(2):187-190
Methods for Computing Numerical Standard Errors: Review and Application to Value-at-Risk Estimation.
Publikováno v:
Journal of Time Series Econometrics; Jul2018, Vol. 10 Issue 2, p1-9, 9p
Publikováno v:
In Economics Letters September 2012 116(3):322-325
Patton and Timmermann (2012, 'Forecast Rationality Tests Based on Multi-Horizon Bounds', Journal of Business & Economic Statistics, 30(1) 1-17) propose a set of useful tests for forecast rationality or optimality under squared error loss, including a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1687::749d03edc7215b32ce1794e2702011bb
https://hdl.handle.net/10419/87513
https://hdl.handle.net/10419/87513
Autor:
Ardia, David, Hoogerheide, Lennart F.
This note presents the R package bayesGARCH (Ardia, 2007) which provides functions for the Bayesian estimation of the parsimonious and effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1687::79a51e889791ec55ccf4d4e2c47c1bad
https://hdl.handle.net/10419/87001
https://hdl.handle.net/10419/87001
Autor:
Hoogerheide, Lennart F., van Dijk, Herman K., van Oest, Rutger D., Belsley, David A., Kontoghiorghes, E.J.
Publikováno v:
Handbook of Computational Econometrics, 215-280
STARTPAGE=215;ENDPAGE=280;TITLE=Handbook of Computational Econometrics
Hoogerheide, L F, van Dijk, H K & van Oest, R D 2009, Simulation-Based Bayesian Econometric Inference : Principles and Some Recent Computational Advances . in D A Belsley & E J Kontoghiorghes (eds), Handbook of Computational Econometrics . John Wiley & Sons, Ltd, pp. 215-280 . https://doi.org/10.1002/9780470748916.ch7
Handbook of Computational Econometrics
STARTPAGE=215;ENDPAGE=280;TITLE=Handbook of Computational Econometrics
Hoogerheide, L F, van Dijk, H K & van Oest, R D 2009, Simulation-Based Bayesian Econometric Inference : Principles and Some Recent Computational Advances . in D A Belsley & E J Kontoghiorghes (eds), Handbook of Computational Econometrics . John Wiley & Sons, Ltd, pp. 215-280 . https://doi.org/10.1002/9780470748916.ch7
Handbook of Computational Econometrics
In this paper we discuss several aspects of simulation based Bayesian econometric inference. We start at an elementary level on basic concepts of Bayesian analysis; evaluating integrals by simulation methods is a crucial ingredient in Bayesian infere
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::63430ffeafb6b5d916998dcd77dfda98
https://doi.org/10.1002/9780470748916.ch7
https://doi.org/10.1002/9780470748916.ch7
Publikováno v:
Ardia, D, Hoogerheide, L F & van Dijk, H K 2009, ' AdMit : Adaptive mixtures of student-t distributions ', The R Journal, vol. 1, no. 1, pp. 25-30 .
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4612::f1866f8d2507d85bb4a9277126d9ddc0
https://hdl.handle.net/1871.1/f67498c9-cd65-4692-a5e7-0ae48713f927
https://hdl.handle.net/1871.1/f67498c9-cd65-4692-a5e7-0ae48713f927
Publikováno v:
Journal of Statistical Software; Vol 29 (2009); 1-32
Journal of Statistical Software, 29(3), 1-32. University of California at Los Angeles
Ardia, D, Hoogerheide, L F & van Dijk, H K 2009, ' Adaptive mixture of Student-t distributions as a flexible candidate distribution for efficient simulation: the R package AdMit ', Journal of Statistical Software, vol. 29, no. 3, pp. 1-32 . https://doi.org/10.18637/jss.v029.i03
Journal of Statistical Software, Vol 29, Iss 3 (2008)
Journal of Statistical Software, 29(3), 1-32. University of California at Los Angeles
Ardia, D, Hoogerheide, L F & van Dijk, H K 2009, ' Adaptive mixture of Student-t distributions as a flexible candidate distribution for efficient simulation: the R package AdMit ', Journal of Statistical Software, vol. 29, no. 3, pp. 1-32 . https://doi.org/10.18637/jss.v029.i03
Journal of Statistical Software, Vol 29, Iss 3 (2008)
This discussion paper resulted in a publication in the Journal of Statistical Software (2009). Vol. 29(3), 1-32. This paper presents the R package AdMit which provides functions to approximate and sample from a certain target distribution given only
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::1818cfec9c00f221893cf5ff8edca7b3
http://papers.tinbergen.nl/08062.pdf
http://papers.tinbergen.nl/08062.pdf