Time Series: Parametric Estimation

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Box-Jenkins Methodology: Building Time Series Models

Covers the Box-Jenkins methodology for building time series models, including model identification, variance calculations, and model diagnostics.

Integrated and Seasonal Processes: Time Series

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

Time Series: Common Models

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

Time Series Models: Autoregressive Processes

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

Linear Regression Basics

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

Time Series: Autoregressive Models

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

Parametric Signal Models: Matlab Practice

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

Parametric Estimation in Time Series

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

Linear Regression Basics

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

Univariate time series: Analysis & Modeling

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