University of Waterloo - ECON 423: Time Series Econometrics (Solutions)

ECON 423: Time Series Econometrics at the University of Waterloo - equips students with a solid econometric foundation for both applied and theoretical research in economics and finance. The course focuses on analyzing data with temporal dependence, introducing popular methods and practices.

✔ Main topics to be covered:

• Probability & Statistics Foundation: The core of this topic is the fundamental concepts and techniques in probability and statistics, which are essential for understanding and analyzing time series data.
• Linear Regression Models: Centered around linear regression models, this topic aims to describe the relationship between a dependent variable and one or more independent variables, assuming a linear relationship between them.
• Concepts of Prices, Returns, & Volatility: This topic emphasizes the understanding of financial asset prices, returns, and their volatility, which is a measure of the dispersion of returns for a given security or market index.
• Linear Time Series Models, Estimation, & Forecasting: Focusing on linear time series models such as autoregressive (AR) and moving average (MA) models, this topic explores the behavior of time series data and how to make forecasts based on historical data.
• Time-Varying Volatility Processes: This topic investigates time-varying volatility processes, such as generalized autoregressive conditional heteroskedasticity (GARCH) models, which are used to model and forecast the volatility of time series data, particularly in financial markets.
• Multivariate Time Series: This topic covers multivariate time series models, such as vector autoregression (VAR) models, which capture the interdependencies between multiple variables in a time series dataset.
• Non-Stationary Models for Time Series & Co-integration: The focus of this topic is on non-stationary models, such as autoregressive integrated moving average (ARIMA) models, which are used to analyze time series data with non-stationary properties. Co-integration is a technique used to analyze the long-run relationship between non-stationary time series.