Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

CSDPD_CP

Financiado

Cerrado

Econometric Modelling of Short Panels with Applications in Financial Econometric...

Econometric Modelling of Short Panels with Applications in Financial Econometrics In recent years, there has been an increased availability of economic datasets that are characterised by a non-negligible amount of time-series information, as well as a large cross-section dimension. Unfortunately, in many cases,... ver más

31/07/2017

BILKENTUNIVERSITY...

100K€

Presupuesto del proyecto: 100K€

Líder del proyecto

BILKENT UNIVERSITESI VAKIF No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2017-07-31 No tenemos la información de la convocatoria

0% 100% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

BILKENTUNIVERSITY BILIM KENT...

0.00€ | Lider

Proyectos interesantes

TEA

Data Science

Applied Physics

1M€ Cerrado

ECO2008-03035

Data Science

Applied Physics

27K€ Cerrado

ECO2014-57007-P

Data Science

Applied Physics

29K€ Cerrado

ECO2015-65758-P

Data Science

Applied Physics

19K€ Cerrado

BUCCAC

Data Science

Applied Physics

2M€ Cerrado

ECO2010-19357

Data Science

Applied Physics

205K€ Cerrado

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Líder del proyecto

BILKENT UNIVERSITESI VAKIF No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 100K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto In recent years, there has been an increased availability of economic datasets that are characterised by a non-negligible amount of time-series information, as well as a large cross-section dimension. Unfortunately, in many cases, although the time-series dimension is not small anymore, it is not large enough for accurate estimation either. Examples of such datasets can be found in a variety of literatures: growth data, firm data (e.g. studies of insider trading activity), earnings studies (the popular Panel Study of Income Dynamics) and hedge fund returns, to name a few. Such panels are generally designated as short panels in the literature. Intuitively, the problem with short panels is a time-series finite-sample bias. This project’s main aim is to conduct a systematic theoretical and empirical research programme on modelling short panels in the presence of unknown and complex types of dependence across both time and cross-section. Inclusion of cross-section dependence is the central novelty of this project. This is motivated by my doctoral research where I showed that this type of dependence introduces new bias terms. The central theoretical objectives are to develop bias correction methods for (i) the Dynamic Autoregressive and Dynamic Probit/Logit models which are quite popular in applied research and (ii) the multivariate nonlinear and dynamic panel data models. The main empirical objectives are to a large extent concerned with univariate and multivariate volatility modelling using panels of financial data. As such, this project will contribute to the theoretical panel data and volatility modelling literatures, as well as the statistics literature, where the outlined small-sample bias has a long research tradition. In addition, as this project is concerned with modelling and understanding macro and financial panels, this project also has a significant potential to contribute to the key policy area Connect to compete: building tomorrow’s networks today.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores