Lectures related to General Linear Processes: Understanding Stationarity and Invertibility

Explores general linear processes, the Wold decomposition theorem, and spectral analysis in time series analysis.

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

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

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

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

Covers linear transformations between polynomial spaces and explores examples of linear independence and bases.

Explores the properties of invertible matrices, including unique solutions and linear independence.

Explores autoregressive models for time series analysis, covering AR(1), AR(2), identification, and MA models.

Explains the concept of least squares solutions and their application in finding the closest solution to a system of equations.

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

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

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

Explores linear models, least squares, Gaussian vectors, and model selection methods.

Explores linear dependence theorems and proofs, emphasizing the importance of understanding linear dependence in linear algebra.

Covers linear algebra basics, including matrices, change of basis, and invertible matrices.

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

Covers parametric estimation, seasonal modeling, Box-Jenkins methods, variance calculations, and dependence measures in time series analysis.

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

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

Explores linear dependence and independence of vectors, including subspaces generation and corollaries.