TOPICS IN APPLIED ECONOMETRICS - 2023/4
Module code: ECO3010
This module builds on the econometrics foundation from the Introductory Econometrics and Intermediate Econometrics courses and emphasizes on introducing micro-econometric techniques used to analyse microeconomic data. The first half of this module considers techniques to achieve causal inference. The second half studies maximum likelihood estimation of limited dependent variable models (logit, probit, Poisson, censoring and selectivity), as well as basic machine learning methods. This will enable students to acquire the skill sets necessary to investigate important and intriguing empirical questions that have real-world impacts on society.
SAHA Nirman (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 6
JACs code: L140
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 84
Lecture Hours: 22
Guided Learning: 44
Prerequisites / Co-requisites
The module builds on students' basic knowledge of econometrics. Indicative content includes:
- An understanding of the `gold standard’ technique of Randomised Control Trials (RCT) with emphasis on the relative strengths and weaknesses and a reflection of the potential problems with designing RCTs. The implication of Conditional Independence Assumption in the context of Matching and Linear Regression Models, investigation of Omitted Variable Bias and Controls that are classified as “Bad Controls”. The empirical applications of this models can be found in the context of college decisions, discrimination in labor market and environmental problems such as effects of global warming.
- How to work with panel data models and derive Difference-in-Difference estimators, understand the basic assumption underlining this strategy. In-depth understanding of Regression Discontinuity design and how to use these techniques to disentangle causal effect of interest from other plausible effects. Hands-on implementation of these techniques in Stata with replication of results from published empirical paper in the econometrics literature.
- Gain in-depth understanding of treatment of limited dependent variables and how OLS assumptions fail to guarantee probabilistic limits on outcome variables and how logit and probit models can be used to solve this. How to work with count data, with in-depth understanding of Poisson regression and corner solutions. In addition, working knowledge of how to work with censored or truncated data and an investigation of the Heckit two-step estimator for empirical application such as labor supply models. A very basic understanding of Machine Learning techniques and LASSO models. These theoretical models and their hands-on implementation on real-world datasets provide students with working knowledge of handling different types of data and variable and enhance research and evaluation skills.
|Assessment type||Unit of assessment||Weighting|
|Coursework||GROUP PROJECT PAPER||50|
|Examination Online||FINAL EXAMINATION||50|
Students will be given an individual paper project if they are unable to complete the group project paper during term.
The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module’s learning outcomes in terms of understanding econometric methods beyond the simple linear regression framework that are commonly used in analysing microeconomic data, and the ability to use relevant Stata packages to investigate real world economic problems
Thus, the summative assessment for this module consists of:
- Coursework project where the students work in groups, worth 50% of the final mark. (Linked to Learning Outcomes 1, 2 and 3).
- Final written exam worth 50% of the final mark. (Linked to Learning Outcomes 4 and 5)
The coursework allows students to demonstrate their ability to work in groups, apply theoretical knowledge on real-world datasets that is based on extensive research, synthesise data and research to evaluate empirical questions and finally communicate results through a well-organized and analysed research project. This strengthens students digital capabilities by using Stata extensively, global and cultural capabilities and employability by interacting and working with other people in groups and developing desirable professional skills. The final exam is designed to test student’s software skills, identify empirical problems when working with data, interpreting results, working under time-constraints with a view to promoting digital capabilities and employability.
Formative assessment and feedback
Students receive verbal feedback during lectures through direct questioning (in which multiple questions and real-world examples of the use of economics are discussed). There are also homework assignments throughout the course, where the model solutions are provided so that students can compare them to their own results. In addition to this, they receive guidance and illustrations to the use of Stata. For midterm assessments, detailed feedback will be provided to individual students along with guidelines on target points which they should emphasize more. This feedback mechanism helps build resourcefulness and resilience among students by providing them more confidence in working with data, through reflection on their own performance and understanding scopes for improvements.
- Enable students to develop a working knowledge of core econometric methods beyond the linear regression model
- Equip the student with the ability to undertake, understand, and critically assess empirical work in economics, with a view to enabling the student to use micro-econometrics to catalogue and describe empirical regularities and test various propositions.
- Enable students to develop critical thinking and data analysis skills through implementation of the empirical techniques on real-world datasets in Stata and prepare them for employment or higher studies
|001||Students will be ableUse Stata to analyse microeconomic datasets using Stata (promotes Digital Capabilities, Employability)||KCPT|
|002||Students will gain working knowledge of empirical models used for causal inference including regression, randomised control trials||KCP|
|003||Students will demonstrate the ability to apply knowledge in (2) to empirical research in the economics literature (promotes Global and Cultural capabilities and Sustainability by learning applications in econometric model identifying discrimination and measuring consequences of climate change)||KPT|
|004||Students will familiarise with modelling limited dependent variables, in particular models for corner solutions, counts, and binary outcomes. Understand the concepts of censoring, corner solutions, truncation, sample selection, and their relationship.||KCPT|
|005||Students will acquire a basic understanding of machine learning methods (promotes employability by enhancing skills in data analysis)||PT|
|006||Students will be able to critically evaluate empirical techniques in econometrics literature, including theory and methods||KCP|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
- Provide students with the opportunity to develop skills in modelling economic problems empirically
- Enable students to appreciate the intuition behind different econometric methods applied in different situations (theory and practice)
- Enable students to learn illustration of theory with “hands-on” implementation with empirical exercise using Stata
The learning and teaching methods include:
- 2-hour lecture per week x 11 weeks
The 2-hour lecture will be a mixed format, where the first hour is dedicated toward understanding the theoretical foundations of each econometric technique, its usefulness, and limitations and the second hour will focus on implementation of these techniques with microeconomic datasets using Stata. There is also weekly guided learning (22 hours), which will focus on reading empirical papers applying the econometric techniques as well as solving problem set exercises which is useful for preparing for the assessments.
The learning and teaching strategy support the development of digital capabilities of students as students get exposed to econometric modelling and interpreting results in Stata. Working with data also enhances their data analysis skills which promotes acquisition of professional skills and hence employability. The second hour of each lecture focuses on empirical applications of theoretical models in important global contexts such as labor market discrimination and climate change consequences which facilitates the development of global and cultural capabilities and sustainability in students.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: ECO3010
The School of Economics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities particularly in the following areas:
The module will work towards understanding models of causal inference which are routinely used in empirical applications focused on identifying discrimination or hidden biases and stereotyping. This module allows students to develop skills that will allow them to effectively collaborate with individuals from around the world.
The module’s empirical application part will focus on application of econometric technique in the field of environmental economics on topics related to climate change and sustainability
Students will further develop the capacity to manage information and datasets pertaining to various types of data. The module requires students use specialist software (STATA) to showcase familiarity with analysing microeconomic datasets.
The module focuses on disseminating knowledge on research techniques and applications to real-world dataset thereby giving them a flavor of data analysis which are professional skills often listed as pre-requisites by employers in the job market. This module allows students to develop skills that will allow them to effectively collaborate with individuals in a professional setting.
Programmes this module appears in
|Economics and Finance BSc (Hons)||2||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Economics BSc (Hons)||2||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Business Economics BSc (Hons)||2||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Economics and Mathematics BSc (Hons)||2||Optional||A weighted aggregate mark of 40% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.