Arbeitspapier
Maximum Likelihood Estimation of a Noninvertible ARMA Model with Autoregressive Conditional Heteroskedasticity
We consider maximum likelihood estimation of a particular noninvertible ARMA model with autoregressive conditionally heteroskedastic (ARCH) errors. The model can be seen as an extension to so-called all-pass models in that it allows for autocorrelation and for more fl exible forms of conditional heteroskedasticity. These features may be attractive especially in economic and financial applications. Unlike in previous literature on maximum likelihood estimation of noncausal and/or noninvertible ARMA models and all-pass models, our estimation theory does allow for Gaussian innovations. We give conditions under which a strongly consistent and asymptotically normally distributed solution to the likelihood equations exists, and we also provide a consistent estimator of the limiting covariance matrix.
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 1226
- Classification
-
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Model Construction and Estimation
- Subject
-
maximum likelihood estimation
autoregressive moving average
ARMA
autoregressive conditional heteroskedasticity
ARCH
noninvertible
noncausal
all-pass
nonminimum phase
Maximum-Likelihood-Schätzung
ARMA-Modell
Heteroskedastizität
Theorie
- Event
-
Geistige Schöpfung
- (who)
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Meitz, Mika
Saikkonen, Pentti
- Event
-
Veröffentlichung
- (who)
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Koç University-TÜSİAD Economic Research Forum (ERF)
- (where)
-
Istanbul
- (when)
-
2012
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Meitz, Mika
- Saikkonen, Pentti
- Koç University-TÜSİAD Economic Research Forum (ERF)
Time of origin
- 2012
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