Lectures related to Model Selection in Time Series Analysis

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

Univariate time series: Analysis & Modeling

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

Explores ARMA models in time series analysis, covering model selection, forecasting, and precision assessment.

Multivariate Time Series: Cointegration & Forecasting

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

Forecasting & Long Memory: Time Series

Explores forecasting methods and long memory in time series analysis.

Time Series

Explores Time Series, covering model specification, diagnostics, and forecasting methods.

Residuals & Forecasting: MATH-342 Time Series

Covers residuals, diagnostics, overfitting, and forecasting methods in time series analysis.

Demand Forecasting: Methods and Models

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

Integrated and Seasonal Processes: Time Series

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

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.

Time Series Forecasting: ARMA Models

Covers ARMA models for time series forecasting, discussing implications, properties of forecast error, challenges with predictions, and covariance models.

Binary Choice Models and Time Series Analysis

Explores binary choice models like probit and logit, as well as univariate time series analysis with ARIMA models for forecasting economic variables.

Vector Autoregression (VAR): Sampling Properties and Examples

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

Time Series: Forecasting and Long Memory

Explores forecasting in time series analysis, long memory processes, and ARCH models for volatility modeling.

Time Series: Autoregressive Models

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

Demand Management: Forecasting Methods

Covers demand management, forecasting methods, and trend analysis in production management.

Vector Autoregression: Modeling Vector-Valued Time Series

Explores Vector Autoregression for modeling vector-valued time series, covering stability, reverse characteristic polynomials, Yule-Walker equations, and autocorrelations.

Time Series Models: Autoregressive Processes

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

Time Series: Parametric Estimation

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

Time Series: Structural Modelling and Kalman Filter

Covers structural modelling, Kalman Filter, stationarity, estimation methods, forecasting, and ARCH models in time series.