Lectures related to Estimation and Forecasting in Time Series

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

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Explores demand forecasting methods, time series analysis, trend forecasting, and the application of the Holt-Winter model.

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

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Covers the concept of parameter estimation using the least square method.

Covers linear regression for estimating train speed using least squares and regularization.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.