Multivariate Time Series: Cointegration & Forecasting

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Instrumental Variables: Addressing Measurement Error and Reverse Causality

Explores how instrumental variables correct biases from measurement error and reverse causality in regression models.

Demand Forecasting: Methods and Models

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

Time Series: Parametric Estimation

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

Time Series: Forecasting and Long Memory

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

Autocorrelation and Instrumental Variables

Explores autocorrelation, instrumental variables, and challenges in regression analysis.

Linear Regression Basics

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

Time Series: Autoregressive Models

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

Time Series: Stochastic Properties and Modelling

Explores the stochastic properties and modelling of time series, covering autocovariance, stationarity, spectral density, estimation, forecasting, ARCH models, and multivariate modelling.

Univariate Time Series Analysis

Explores univariate time series analysis, covering stationarity, ARMA processes, model selection, and unit root tests.

Residuals & Forecasting: MATH-342 Time Series

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