Multivariate contemporaneous ARMA model with hydrological applications
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Publication:1111834
DOI10.1007/BF01543810zbMath0658.76006MaRDI QIDQ1111834
Keith William Hipel, A. Ian McLeod, Franklin Camacho
Publication date: 1987
Published in: Stochastic Hydrology and Hydraulics (Search for Journal in Brave)
maximum likelihood estimationtime series analysiscontemporaneous autoregressive moving average modelsmultivariate modellingstochastic hydrology
Hydrology, hydrography, oceanography (86A05) Basic methods in fluid mechanics (76M99) Foundations, constitutive equations, rheology, hydrodynamical models of non-fluid phenomena (76A99)
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Cites Work
- Gains in efficiency from joint estimation of systems of autoregressive- moving average processes
- Causality in temporal systems. Characterizations and a Survey
- Simple correlated arma processes
- Likelihood Function of Stationary Multiple Autoregressive Moving Average Models
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- Predictions of multivariate autoregressive-moving average models
- Vector linear time series models
- Checking the Independence of Two Covariance-Stationary Time Series: A Univariate Residual Cross-Correlation Approach
- Identification of Dynamic Regression (Distributed Lag) Models Connecting Two Time Series
- Improved Box-Jenkins Estimators
- An algorithm for the exact likelihood of a mixed autoregressive-moving average process
- Contemporaneous bivariate time series
- Investigating Causal Relations by Econometric Models and Cross-spectral Methods
- On the error of prediction of a time series
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