Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/40367
Título: Mixture inverse Gaussian for unobserved heterogeneity in the autoregressive conditional duration model
Autores/as: Gómez–Déniz, Emilio 
Pérez–Rodríguez, Jorge V. 
Clasificación UNESCO: 53 Ciencias económicas
5302 Econometría
Palabras clave: Autoregressive conditional duration model
Finite mixtures
Inverse Gaussian distribution
Fecha de publicación: 2017
Publicación seriada: Communications in Statistics - Theory and Methods 
Resumen: In this paper, we assume that the duration of a process has two different intrinsic components or phases which are independent. The first is the time it takes for a trade to be initiated in the market (for example, the time during which agents obtain knowledge about the market in which they are operating and accumulate information, which is coherent with Brownian motion) and the second is the subsequent time required for the trade to develop into a complete duration. Of course, if the first time is zero then the trade is initiated immediately and no initial knowledge is required. If we assume a specific compound Bernoulli distribution for the first time and an inverse Gaussian distribution for the second, the resulting convolution model has a mixture of an inverse Gaussian distribution with its reciprocal, which allows us to specify and test the unobserved heterogeneity in the autoregressive conditional duration (ACD) model.Our proposals make it possible not only to capture various density shapes of the durations but also easily to accommodate the behaviour of the tail of the distribution and the non monotonic hazard function. The proposed model is easy to fit and characterizes the behaviour of the conditional durations reasonably well in terms of statistical criteria based on point and density forecasts.
URI: http://hdl.handle.net/10553/40367
ISSN: 0361-0926
DOI: 10.1080/03610926.2016.1200094
Fuente: Communications in Statistics - Theory and Methods[ISSN 0361-0926],v. 46, p. 9007-9025
Colección:Artículos
Vista completa

Citas SCOPUSTM   

2
actualizado el 24-mar-2024

Citas de WEB OF SCIENCETM
Citations

2
actualizado el 25-feb-2024

Visitas

44
actualizado el 30-sep-2023

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.