Artikel
On robust estimation of negative binomial INARCH models
We discuss robust estimation of INARCH models for count time series, where each observation conditionally on its past follows a negative binomial distribution with a constant scale parameter, and the conditional mean depends linearly on previous observations. We develop several robust estimators, some of them being computationally fast modifications of methods of moments, and some rather efficient modifications of conditional maximum likelihood. These estimators are compared to related recent proposals using simulations. The usefulness of the proposed methods is illustrated by a real data example.
- Sprache
-
Englisch
- Erschienen in
-
Journal: METRON ; ISSN: 2281-695X ; Volume: 79 ; Year: 2021 ; Issue: 2 ; Pages: 137-158 ; Milan: Springer
- Klassifikation
-
Mathematik
Foreign Aid
General Financial Markets: General (includes Measurement and Data)
- Thema
-
Count time series
Negative binomial distribution
Overdispersion
Generalized linear models
Rank autocorrelation
Tukey M-estimator
Additive outliers
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Elsaied, Hanan
Fried, Roland
- Ereignis
-
Veröffentlichung
- (wer)
-
Springer
- (wo)
-
Milan
- (wann)
-
2021
- DOI
-
doi:10.1007/s40300-021-00207-8
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Elsaied, Hanan
- Fried, Roland
- Springer
Entstanden
- 2021
Ähnliche Objekte (12)
Negative binomial Kumaraswamy-G cure rate regression model
Robust estimation in econometric models
Robust estimation in nonlinear regression models
Robust estimation in nonlinear regression models
Robust Estimation in Nonlinear Regression Models
BORROWING INFORMATION OVER TIME IN BINOMIAL/LOGIT NORMAL MODELS FOR SMALL AREA ESTIMATION
Estimation of adjusted relative risks in log-binomial regression
Robust estimation of zero-inflated count models
Robust Estimation of Probit Models with Endogeneity
Robust parameter estimation for ARMA models : = Robuste Parameterschätzung in ARMA-Modellen
Emergence of Independent Candidates:A Negative Binomial Regression Model of anIndian Parliamentary Election
Consistency for the negative binomial regression with fixed covariate
Negative binomial Kumaraswamy-G cure rate regression model
Robust estimation in econometric models
Robust estimation in nonlinear regression models
Robust estimation in nonlinear regression models
Robust Estimation in Nonlinear Regression Models
BORROWING INFORMATION OVER TIME IN BINOMIAL/LOGIT NORMAL MODELS FOR SMALL AREA ESTIMATION
Estimation of adjusted relative risks in log-binomial regression
Robust estimation of zero-inflated count models
Robust Estimation of Probit Models with Endogeneity
Robust parameter estimation for ARMA models : = Robuste Parameterschätzung in ARMA-Modellen
Emergence of Independent Candidates:A Negative Binomial Regression Model of anIndian Parliamentary Election
Consistency for the negative binomial regression with fixed covariate
Negative binomial Kumaraswamy-G cure rate regression model
Robust estimation in econometric models
Robust estimation in nonlinear regression models
Robust estimation in nonlinear regression models
Robust Estimation in Nonlinear Regression Models
BORROWING INFORMATION OVER TIME IN BINOMIAL/LOGIT NORMAL MODELS FOR SMALL AREA ESTIMATION
Estimation of adjusted relative risks in log-binomial regression
Robust estimation of zero-inflated count models
Robust Estimation of Probit Models with Endogeneity
Robust parameter estimation for ARMA models : = Robuste Parameterschätzung in ARMA-Modellen
Emergence of Independent Candidates:A Negative Binomial Regression Model of anIndian Parliamentary Election