Author: Gabriele Fiorentini
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
We investigate the effects of dynamic heteroskedasticity on statistical factor analysis. We show that identification problems are alleviated when variation in factor variances is accounted for. Our results apply to dynamic APT models and other structural models. We also find that traditional ML estimation of unconditional variance parameters remains consistent if the factor loadings are identified from the unconditional distribution, but their standard errors must be robustified. We develop a simple preliminary LM test for ARCH effects in the common factors, and discuss two-step consistent estimation of the conditional variance parameters. Finally, we conduct a detailed simulation exercise.
Identification, Estimation and Testing of Conditionally Heteroskedastic Factor Models
Author: Gabriele Fiorentini
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
We investigate the effects of dynamic heteroskedasticity on statistical factor analysis. We show that identification problems are alleviated when variation in factor variances is accounted for. Our results apply to dynamic APT models and other structural models. We also find that traditional ML estimation of unconditional variance parameters remains consistent if the factor loadings are identified from the unconditional distribution, but their standard errors must be robustified. We develop a simple preliminary LM test for ARCH effects in the common factors, and discuss two-step consistent estimation of the conditional variance parameters. Finally, we conduct a detailed simulation exercise.
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
We investigate the effects of dynamic heteroskedasticity on statistical factor analysis. We show that identification problems are alleviated when variation in factor variances is accounted for. Our results apply to dynamic APT models and other structural models. We also find that traditional ML estimation of unconditional variance parameters remains consistent if the factor loadings are identified from the unconditional distribution, but their standard errors must be robustified. We develop a simple preliminary LM test for ARCH effects in the common factors, and discuss two-step consistent estimation of the conditional variance parameters. Finally, we conduct a detailed simulation exercise.
Identification of multivariate conditionally heteroskedastic factor models
The Relation Between Conditionally Heteroskedastic Factor Models and Factor GARCH Models
Author: Enrique Sentana Iváñez
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
Book Description
Conditionally Heteroskedastic Factor Models
Author: Catherine Doz
Publisher:
ISBN:
Category : Business enterprises
Languages : en
Pages : 0
Book Description
Publisher:
ISBN:
Category : Business enterprises
Languages : en
Pages : 0
Book Description
Conditionally Heteroskedastic Factor Models : Identification and Instrumental Variables Estimation
Author: Renault, Éric
Publisher: Montréal : CIRANO
ISBN:
Category :
Languages : en
Pages : 55
Book Description
Publisher: Montréal : CIRANO
ISBN:
Category :
Languages : en
Pages : 55
Book Description
An em-based algorith for conditionally heteroskedastic factor models
The Relation Between Conditionally Heteroskedastic Factor Models and Factor Garch Models
Indirect Estimation of Conditionally Heteroskedastic Factor Models
An em-based algorithm for conditionally heteroskedastic factor models
Author: Antonis Demos
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 38
Book Description
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 38
Book Description