Vector Autoregression: Modeling Vector-Valued Time Series

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Time Series: Linear Filtering and Spectral Estimation

Explores linear filtering, spectral estimation, and second-order stationarity 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.

Financial Time Series: ARCH and GARCH Models

Covers regression analysis, multivariate linear regression, principal component analysis, and factor models.

Signal Processing Fundamentals

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

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.

Time Series: Autoregressive Models

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

Long Memory and ARCH: Time Series

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

Autocorrelation and Instrumental Variables

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

Instrumental Variables: Addressing Measurement Error and Reverse Causality

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

Univariate Time Series Analysis

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