Vector Autoregression: Modeling Vector-Valued Time Series
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Explores the stochastic properties and modelling of time series, covering autocovariance, stationarity, spectral density, estimation, forecasting, ARCH models, and multivariate modelling.
Covers model selection, diagnostics, and forecasting in time series analysis, emphasizing the challenges of determining the model order based on autocorrelation and partial autocorrelation functions.
