Estimation and Forecasting in Time Series

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Related lectures (28)

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Covers global climate issues, impacts, policies, and mechanisms to slow down climate change through group work and project management.

Parametric Estimation in Time Series

Covers parametric estimation in time series analysis, including integrated processes and seasonal modeling.

Integrated and Seasonal Processes: Time Series

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

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Linear Regression: Basics and Estimation

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Time Series: Parametric Estimation

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

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Model Specification in Time Series

Covers the identification and model specification in time series analysis, including AR models and least squares estimation.

Linear Regression: Estimation and Prediction

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

Regression: Simple and Multiple Linear

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