Econometrics 2 continues with the knowledge trajectory built up in the Econometrics 1 course by developing advanced concepts and skills in statistical methods used for applied economics (and in general the social sciences). The course content expands cross-sectional methods to panel data, introduces the generalized method of moments (GMM) estimator, and provides introductions to the topics of causal inference, time series methods, and non-linear models.

Synchronous:

10 x 2-hour lectures on campus/online as appropriate for the course content

10 x 2-hour labs on campus/online as appropriate for the course content

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ILO being assessed

Econometrics 2 furthers the understanding of Quantitative Methods by introducing advanced techniques used for learning and testing economic phenomena on real-world data. The course begins by providing extensions of classical cross-sectional methods to panel or longitudinal data, where the analyst has access to additional information of units repeated across several samples. Given the growing amount of data of this kind made available by institutions across the world, it is a crucial step in bridging the gap between the assumptions made by the methods in the basic methods and the messy reality of economic data.

In addition to mastering econometric methodologies, students develop key research and digital skills by using software to implement advanced econometric models. A strong emphasis is placed on the reality of big data, providing students with the tools required to study the computational complexity of implemented methodologies. This emphasis enables students to reap the benefits and face the challenges presented by these innovations in the context of econometric analysis. This future-oriented focus ensures students are prepared for the data-driven landscape they will encounter in future studies and professional environments.

By combining both advanced theoretical knowledge through the lectures and hands-on implementation of statistical software in the tutorials, the course expands the toolkit students have available to describe and learn from data. Exercises in-class and assessments are geared towards providing students the opportunity to practice this combination of skills. These activities simulate global economic challenges and foster experiential learning by connecting theory with practice, preparing students for further academic research or professional roles that require advanced econometric knowledge. 

By the end of this course students will be able to:

1. Critically analyse a wide range of the theoretical and practical issues associated with econometric models.

2. Identify, conceptualize, define and motivate a series of estimators and estimation methodologies/algorithms, and their optimal use in various empirical scenarios.

3. Demonstrate extensive, detailed and critical analysis skills in the use of econometric methods for empirical research, engaging with concepts and ideas discussed in articles at the research frontier.

4. Solve significant specialized applied problems, creatively using a wide range of computer-based packages

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.