Archive Module Description
No such Code for pgprog: N3I101
No such Code for pgprog: N3I201
No such Code for pgprog: N4I101
Department: Economics and Finance
ECON47515: Advanced Research Methods - Part I
|Type||Tied||Level||4||Credits||15||Availability||Not available in 2021/22|
Excluded Combination of Modules
- To provide a rigorous treatment of advanced statistics and econometrics and their applications to modelling and testing;
- To give students advanced knowledge in conducting various types of statistical analyses;
- To give students important conceptual information about issues underlying the use of a variety of quantitative techniques in research;
- To ensure that students are aware of neighbouring methods that they can take advantage of for advanced quantitative study and data analysis;
- To develop studentsâ€™ ability to identify suitable analytical methods to answer their research questions.
- The core content of this module will be topics in advanced statistics and econometrics.
- Indicative topics are : â€¢ Probability theory â€¢ Sampling statistics and distribution â€¢ Asymptotic theory â€¢ Nonparametric tests â€¢ Bayesian analysis â€¢ Panel data analysis
- Have advanced and up-to-date knowledge of the principles and methods of modern financial econometrics and data analysis;
- Have a comprehensive understanding of neighbouring analytical methods and the ability to robustly argue for and against the use of a particular approach;
- Have advanced understanding of quantitative analytical techniques and interpretation of the results;
- Have a comprehensive understanding of key methodological considerations and challenges for quantitative data generation or collection required for a particular analysis method to be potent;
- Have extended their understanding of the use of econometric tools to conduct advanced empirical investigations into complex specialised issues.
- Ability to skilfully conduct quantitative research and statistical analysis;
- Ability to understand state of the art econometric tools for analysing data;
- Ability to defend chosen methods and objectively critique the application of alternative quantitative methods of research and analysis.
- The ability to organize research and analyze data to answer a particular research question;
- The ability to appropriately model and analyze complex data generating characteristics;
- The ability to design the data analysis;
- The ability to carefully evaluate the data requirements underpinning particular quantitative methods and to consider the actions needed to execute complex studies.
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- The module will be delivered in a series of 10 lectures and 10 seminars during Term 1.
- Learning will also occur through tutor-supported, as well as self-support learning groups thus enabling students to develop their own effective research methods strategies.
- The assessment of the module is by a 3,500 word written assignment based on evaluating critical success factors tied to the successful acquisition of robust data for advanced quantitative data analysis and the selection and execution of appropriate analysis techniques.
Teaching Methods and Learning Hours
|Self-supported learning groups (students are expected to form their own discussion groups to reflect on and share their learning about the issues raised in the module)||10||Weekly||1 hour||10|
|Independent study, preparation and reading||110|
|Component: Written assignment||Component Weighting: 100%|
|Element||Length / duration||Element Weighting||Resit Opportunity|
|Individual written assignment||3,500 words||100%||same|
Students will be required to submit a 1,000 word planning document outlining aspects of advanced quantitative research methods and data analysis relevant to their research area.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University