Time Series: Fundamentals and Models

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Related lectures (32)

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

Forecasting & Long Memory: Time Series

Explores forecasting methods and long memory in time series analysis.

Integrated and Seasonal Processes: Time Series

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

Time Series: Representation and Modelling

Covers the stochastic properties of time series, stationarity, autocovariance, special stochastic processes, spectral density, digital filters, estimation techniques, model checking, forecasting, and advanced models.

Time Series: Common Models

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

Multivariate Time Series: Cointegration & Forecasting

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

Time Series: Parametric Estimation

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

Long Memory and ARCH: Time Series

Explores long memory in time series and ARCH models for financial volatility.

Univariate time series: Analysis & Modeling

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

Count Data Models & Univariate Time Series Analysis

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