Multivariate Time Series: Cointegration & Forecasting

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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.

Time Series Models: Autoregressive Processes

Explores time series models, emphasizing autoregressive processes, including white noise, AR(1), and MA(1), among others.

Vector Autoregression (VAR): Sampling Properties and Examples

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

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.

Binary Choice Models and Time Series Analysis

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

Demand Management: Forecasting Methods

Covers demand management, forecasting methods, and trend analysis in production management.

Time Series: Fundamentals and Models

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

Vector Autoregression

Explores Vector Autoregression for modeling vector-valued time series, covering stability, Yule-Walker equations, and spectral representation.

Time Series Forecasting: ARMA Models

Covers ARMA models for time series forecasting, discussing implications, properties of forecast error, challenges with predictions, and covariance models.