Time Series: Stochastic Properties and Modelling

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Time Series: Representation and Modelling

Covers the stochastic properties of time series, stationarity, autocovariance, special stochastic processes, spectral density, digital filters, estimation techniques, model checking, forecasting, and advanced models.

Time Series: Structural Modelling and Kalman Filter

Covers structural modelling, Kalman Filter, stationarity, estimation methods, forecasting, and ARCH models in time series.

Integrated and Seasonal Processes: Time Series

Explores parametric estimation, integrated processes, seasonal modeling, and ARIMA model building in time series analysis.

Parametric Signal Models: Matlab Practice

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.

Multivariate Time Series: Cointegration & Forecasting

Explores multivariate time series analysis, cointegration, forecasting with ARMA models, and practical applications in interest rates analysis.

Multivariate Time Series and Spectral Representation

Explores multivariate time series analysis, emphasizing spectral representation and estimation methods.

Time Series: Fundamentals and Models

Covers the fundamentals of time series analysis, including models, stationarity, and practical aspects.

Vector Autoregression (VAR): Sampling Properties and Examples

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

Forecasting & Long Memory: Time Series

Explores forecasting methods and long memory in time series analysis.

Vector Autoregression: Modeling Vector-Valued Time Series

Explores Vector Autoregression for modeling vector-valued time series, covering stability, reverse characteristic polynomials, Yule-Walker equations, and autocorrelations.