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Extensions of the multiple regression model are examined. Topics include causal and statistical interpretations of regression models, instrumental variables, panel data and time series regression models and relevant statistical theory.
Intended learning outcomes
On successful completion of this subject students should be able to:
- Explain various problems of interpretation of causal estimates in regression and how the problems may be addressed using instrumental variables.
- Apply least squares methods to estimation and inference for linear regression models with panel data.
- Apply least squares to estimation, inference and interpretation for single and multiple equation models for stationary and non-stationary time series data.
- Derive, simulate and interpret statistical properties of least squares estimators in these settings.
- Formulate hypotheses about economic phenomena, collect real-world data, apply econometric methods to test hypotheses, and draw evidenced-based conclusions or implications for economic theory and/or public policy.
- High level of development: problem solving; statistical reasoning; application of theory to practice; interpretation and analysis; evaluation of data and other information; use of computer software.
- Moderate level of development: written communication; collaborative learning; team work; critical thinking; synthesis of data and other information.
- Some level of development: accessing data and other information from a range of sources.
Last updated: 1 December 2023