Model Specification in Time Series

Related lectures (30)

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Explores estimation, forecasting, and model comparison in time series analysis using real data examples to motivate the study.

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

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

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Covers the basics of linear regression, focusing on estimation and prediction.

Covers the concept of least square estimation and its real-world applications.

Covers linear regression, regularization, and probabilistic models in generating labels.

Covers linear regression topics including confidence intervals, variance, and maximum likelihood approach.