Arbeitspapier
A note on an iterative least squares estimation method for ARMA and VARMA models
In this note we suggest a new iterative least squares method for estimating scalar and vector ARMA models. A Monte Carlo study shows that the method has better small sample properties than existing least squares methods and compares favourably with maximum likelihood estimation as well.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 467
- Klassifikation
-
Wirtschaft
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
ARMA models
Theorie
ARMA-Modell
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Kapetanios, George
- Ereignis
-
Veröffentlichung
- (wer)
-
Queen Mary University of London, Department of Economics
- (wo)
-
London
- (wann)
-
2002
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 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
- Arbeitspapier
Beteiligte
- Kapetanios, George
- Queen Mary University of London, Department of Economics
Entstanden
- 2002
Ähnliche Objekte (12)
On testing for diagonality of large dimensional covariance matrices
Unit root testing against the alternative hypothesis of up to m structural breaks
A new nonparametric test of cointegration rank
Dynamic factor extraction of cross-sectional dependence in panel unit root tests
Measuring conditional persistence in time series
Testing for strict stationarity
Cluster analysis of panel datasets using non-standard optimisation of information criteria
Using extraneous information and GMM to estimate threshold parameters in TAR models
A bootstrap invariance principle for highly nonstationary long memory processes
A note on covariance stationarity conditions for dynamic random coefficient models
A bootstrap procedure for panel datasets with many cross-sectional units
Choosing the optimal set of instruments from large instrument sets
On testing for diagonality of large dimensional covariance matrices
Unit root testing against the alternative hypothesis of up to m structural breaks
A new nonparametric test of cointegration rank
Dynamic factor extraction of cross-sectional dependence in panel unit root tests
Measuring conditional persistence in time series
Testing for strict stationarity
Cluster analysis of panel datasets using non-standard optimisation of information criteria
Using extraneous information and GMM to estimate threshold parameters in TAR models
A bootstrap invariance principle for highly nonstationary long memory processes
A note on covariance stationarity conditions for dynamic random coefficient models
A bootstrap procedure for panel datasets with many cross-sectional units
Choosing the optimal set of instruments from large instrument sets
On testing for diagonality of large dimensional covariance matrices
Unit root testing against the alternative hypothesis of up to m structural breaks
A new nonparametric test of cointegration rank
Dynamic factor extraction of cross-sectional dependence in panel unit root tests
Measuring conditional persistence in time series
Testing for strict stationarity
Cluster analysis of panel datasets using non-standard optimisation of information criteria
Using extraneous information and GMM to estimate threshold parameters in TAR models
A bootstrap invariance principle for highly nonstationary long memory processes
A note on covariance stationarity conditions for dynamic random coefficient models
A bootstrap procedure for panel datasets with many cross-sectional units