ECONOMETRICS 1 - 2022/3
Module code: ECOM042
This module is an introduction to the methods of specification, estimation and testing of econometric models in a general multivariate setting. The techniques are applied to real data making use of the econometric packages.
VOLPICELLA Alessio (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: L140
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 92
Lecture Hours: 20
Laboratory Hours: 9
Captured Content: 29
Prerequisites / Co-requisites
Indicative content includes:
- Multiple Regression analysis using cross sectional data
- Asymptotic properties of OLS
- Regression analysis using qualitative information
- Functional form
- Instrumental variables estimation
- Econometric models with time series
|Assessment type||Unit of assessment||Weighting|
|Online Scheduled Summative Class Test||CLASS TEST||30|
The assessment strategy is designed to provide students with the opportunity to demonstrate their ability to understand and carry out econometric techniques. In particular, assessment features real-word problem-based tasks, and as such it aims at improving resourcefulness and resilience of students.
This module has a technical and a practical component. As such, assessment put emphasis on both aspects in the form of a midterm and coursework exercise, in which students are asked to analyse real economic and financial data with a statistical software.
Thus, the summative assessment for this module consists of:
- class test, worth 30% of the final module mark;
- coursework exercise, worth 70% of the final module mark.
Formative assessment and feedback
Students receive verbal feedback during lectures and tutorials through direct questioning (in which multiple questions and real-world examples of the use of economics are discussed). In addition to this, they receive guideline solutions to tutorial questions, against which they can compare their own results. After the midterm feedback is provided for all individual questions.
- Provide the student with the theoretical and practical skills necessary to construct state of the art, single and multi-equation econometric models. The module will 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 econometrics to catalogue and describe empirical regularities and test various propositions.
|001||Systematically understand the principles of estimation and hypothesis testing in a multivariate setting||KCT||EMPLOYABILITY|
|002||Demonstrate comprehensive knowledge of the properties of different estimators and tests||KCT|
|003||Demonstrate a practical understanding of the application of econometric techniques to actual data||KCPT||DIGITAL CAPABILITIES; EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE|
|004||Be critically aware of the assumptions made in building econometric models||KCT|
|005||Write up the results of a study of an economic problem that includes econometric analysis, demonstrating the ability to communicate clearly their findings and evaluate critically state-of-the-art empirical research in that field of economics||KCPT||EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE|
|006||Proficiently use the testing and estimation capabilities of statistical software, evaluating the relative merits||KCPT||DIGITAL CAPABILITIES|
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 improve digital capabilities, employability, and resourcefulness and resilience through full use of statistical software, and real world examples and datasets.This prepares students for the study of economics and econometrics at FHEQ Level 7 (first week); gives students the theoretical tools they need to go out and analyse real world situations; and encourage hands-on study of empirical problems.
The learning and teaching methods include:
- Readings using lecturers guidance
- Solving exercises
- Responding to questions in class
- Preparing and taking part in the test
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: ECOM042
In line with the University's curriculum framework, 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 in the following areas:
Full use of statistical software in both teaching, especially in lab classes and learning activities, and assessment.
Real-world examples and dataset throughout lectures, learning activities and assessment, statistical software.
Resourcefulness and Resilience
Assessment features real-world problem-based tasks that are designed to enhance student resourcefulness.
Programmes this module appears in
|Economics MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Economics (International Economics) MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Economics (Macroeconomics and Financial Markets) MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Economics (Policy Evaluation and Data Analysis) MSc||1||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Economics and Finance MSc||1||Compulsory||A weighted aggregate mark of 50% 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 2022/3 academic year.