University of Manitoba - ECON 4490: Macroeconometrics for Policy Analysis

Macroeconometrics for Policy Analysis (ECON 4490) - is an introductory course in time series methods with a focus on empirical macroeconomic applications for policy analysis. The course provides a substantive foundation for further study of more advanced topics in time series as well as macroeconomic theory and policy subjects.

Main topics to be covered:

• Difference Equations: Introducing first-order, second-order, and higher-order difference equations, the focus is on modeling time series data and predicting future values.
• Stationary Univariate Time Series Models: Delving into various stationary univariate time series models, the discussion encompasses autoregressive (AR) models, moving average (MA) models, autoregressive moving average (ARMA) models, and autoregressive integrated moving average (ARIMA) models.
• Univariate Models of Heteroskedasticity: Addressing models that account for changing variances in time series data, this section explores autoregressive conditional heteroskedasticity (ARCH) models, generalized autoregressive conditional heteroskedasticity (GARCH) models, exponential GARCH models, and stochastic volatility models.
• Unit Root Tests: Presenting different tests for unit roots in time series data, this part includes the Dickey-Fuller test, the augmented Dickey-Fuller test, the Phillips-Perron test, and the KPSS test.
• Vector Autoregression and Vector Error-Correction Models: Focusing on multivariate time series models, this section specifically covers vector autoregression (VAR) models and vector error-correction (VEC) models.
• Cointegration and Error-Correction Models: Discussing cointegration and error-correction models, this part includes the Engle-Granger test and the Johansen test.
• Nonlinear Models and Breaks: Introducing nonlinear time series models such as threshold autoregressive (TAR) models, smooth transition autoregressive (STAR) models, and Markov switching models, this section also covers methods for detecting structural breaks in time series data.
• Multivariate GARCH Models: Covering multivariate GARCH models, this section includes the constant conditional correlation (CCC) model, the dynamic conditional correlation (DCC) model, and the BEKK model.

ACC 1100  

ACC 2010  

ACC 2020