|Year of offer||2018|
|Subject level||Graduate coursework|
|Fees||Subject EFTSL, Level, Discipline & Census Date|
This subject examines the use of macroeconomic models in economic policy analysis. The overall aim of the course is to discuss system approaches to estimating the macro-economy. Special attention will be paid to the SVAR and DSGE approaches and the relationship between them. The course will also include a discussion of optimal and unconventional policies. Topics include: impulse response functions, policy multipliers; policy simulation techniques and sensitivity analysis of economy-wide models. Applications to Australia will also be discussed.
Intended learning outcomes
On successful completion of this subject students should be able to:
- Specify and estimate VAR/SVAR models;
- Generate and understand impulse response functions and forecast error variance decompositions;
- Understand alternative identification strategies and apply them to identify monetary policy shocks, fiscal shocks and technology shocks;
- Specify a basic New Keynesian model; generalise it for a small open economy as well as other commonly included extensions in empirical applications;
- Apply econometrics methods (Generalized Method of Moments and the Kalman filter) commonly used to estimate DSGE models;
- Analyse policies by simulating DSGE models;
- Understand the relevance of optimal and unconventional policies;
- Evaluate recent central bank and academic research using VAR/SVAR and small-open economy DSGE models.
On successful completion of this subject, students should have improved the following generic skills:
- high level of development: statistical reasoning; application of theory to practice; interpretation and analysis; critical thinking; synthesis of data and other information; receptiveness to alternative ideas.
- moderate level of development: oral communication; written communication; collaborative learning; problem solving; evaluation of data and other information; use of computer software.
- some level of development: team work; accessing data and other information across a range sources.