Lectures related to Time Series: Stochastic Properties and Modelling

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.

Time Series: Fundamentals and Models

Explores the fundamentals of time series analysis, including stationarity, linear processes, forecasting, and practical aspects.

Univariate time series: Analysis & Modeling

Covers the analysis and modeling of univariate time series, focusing on stationarity, ARMA processes, and forecasting.

Signal Processing Fundamentals

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Time Series: Linear Filtering and Spectral Estimation

Explores linear filtering, spectral estimation, and second-order stationarity in time series analysis.

Count Data Models & Univariate Time Series Analysis

Covers count data models and Poisson regression, then transitions to univariate time series analysis for forecasting economic variables.

Spectral Estimation: Periodogram and Tapering

Explores spectral representations, ACVS estimation, and spectral estimation in time series analysis.

Spectral Analysis: Integrated Spectrum and Autocovariance

Explores spectral analysis, integrated spectrum, autocovariance, estimation, and convergence in time series models.

Model Choice and Prediction

Explores model choice, prediction, and forecasting techniques in time series analysis.

Linear Estimation & Prediction: Models & Methods

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

Time Series: Multi-Tapering and Parametric Estimation

Covers Multi-Tapering and Parametric Estimation in Time Series analysis, including spectral estimation and AR model fitting.