Time Series: Structural Modelling and Kalman Filter

Related lectures (31)

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Explores Time Series, covering model specification, diagnostics, and forecasting methods.

Covers Vector Autoregression (VAR) in time series analysis, including sampling properties and examples of VAR processes.

Explores ARMA models in time series analysis, covering model selection, forecasting, and precision assessment.

Explores binary choice models like probit and logit, as well as univariate time series analysis with ARIMA models for forecasting economic variables.

Explores univariate time series analysis, covering stationarity, ARMA processes, model selection, and unit root tests.

Explores structural modelling in time series and introduces the Kalman filter for prediction and estimation.

Explores linear estimation and prediction in AR parametric models, focusing on Yule Walker equations and Wiener filter.

Explores spectral analysis, aliasing, and linear filtering in time series data.

Covers common time series models, trend removal, and seasonality adjustment techniques.

Covers the Box-Jenkins methodology for building time series models, including model identification, variance calculations, and model diagnostics.