Business School London campus
Session 2023-24 Trimester 2
Module Code: LNDN08003 DATA ANALYTICS FINAL PROJECT
Due Date: 12th APRIL 2024
Answer ALL
questions.
LNDN08003–Data Analytics
Group Empirical Research Project
Question 2-The project (2500 maximum word limit)
The datasets for this assignment should be downloaded from the World Development Indicators (WDI) of the World Bank. The purpose of the report is to further explore how FDI affects the GDP of your chosen country.
A). Run a regression with all possible predictor variables included in the model. Interpret one of the parameters estimates in context of the data and test the hypothesis that the associated population parameter is equal to zero. Provide regression output for the full model in your answer.
Calculate i) the summary statistics, ii) the correlation matrix, iii) the time series plot
for all the variables.
Eliminate one of the predictor variables and provide the new regression output. Do the parameter estimate change? Does the R-squared or the adjusted R-squared change? Briefly describe (in general) why these changes might occur.
D). Include all the excel outputs in your report.
Data: Note that the dataset to complete the project should contain all the variables are either averages over a 7-year period (2017-2022) or for some year in that period.
• GDP growth = GDP (current US$).
FDI=Foreign Direct Investment (Foreign direct investment, net (BoP, current US$))
• REER=Real Effective Exchange Rate (index (2010 = 100)
Note: Please only work on the country assigned to your group as working on another country will result in a zero grade.
The goal of this project is for you to develop an understanding of how to use data to conduct applied empirical research. To this end, you should:
demonstrate an understanding of Excel syntax, data management skills, and best documentation practices.
demonstrate the ability to place a research question in the context of existing scholarly discourse through an effective literature review; and
demonstrate an understanding of the necessary components of a well- written empirical research paper and formatting and style conventions.
Your paper or report should examine question 2 broadly defined. Papers that simply rehash class material will receive poor grades; good papers will apply the empirical tools in a rigorous and thoughtful manner. Your regression methodology is already chosen for you and do not need to do anything groundbreaking, just well done and complete, but you should be identifying and contributing to a scholarly discussion.
The reader should be able to easily recognize the role or purpose, audience, format, and task (RAFT) of your paper. The audience of your report should be an informed reader, colleague, or peer. You should be a participant in a scholarly conversation. As such, your writing should be academic, technical, maintain a critical distance from the examination, and refrain from personal narratives. Your task should be framed as an interesting problem. Data Analysts/Economists are concerned with analyzing data to test the plausibility of economic theory or develop new understandings of human behaviour and its consequences for social well-being.
Often, data analysts use their analyses to provide policy prescriptions and suggestions for improving welfare. As such, your essay should explain to the readerwhy the topic is important and the motivation for the analysis.
The other guiding principle of this project is the belief that providing comprehensive replication documentation for research involving statistical data should be as ubiquitous and routine as providing a list of references.
SUGGESTED OUTLINE OF THE FINAL PROJECT
Introduction
State clearly the research question you are investigating. Indicate how you approach the problem (outline the structure of your answer).
Background (Literature Review)
Describe the underlying theory (if applicable) and findings of important related literature. Identify any original contribution you believe your study could make.
Model
You must centre your project on one or a number of relationships / propositions suitable for statistical estimation and/or testing. This should be related to relevant theory (where appropriate). In this section you should write out the regression equation / model you will be estimating.
Data
Carefully define your variables; include a list of expected signs and a table of descriptive statistics. Some preliminary charts and diagrams should also be included.
Empirical Analysis
The analysis of the results is a key stage of the study. It involves estimating an initial model or models, performing appropriate diagnostic tests, reformulating and re-estimating, conducting tests of hypothesis and, finally, prediction and policy implications. You should consider including a comparative table of results (in terms of different estimation methods, different samples and / or variables) – basically whatever you think is appropriate given the research topic/question you are considering. Make sure the results are interpreted appropriately.
Conclusion
Summarise your findings and discuss their implications (e.g. are there any policy implications associated with your results?). Discuss the limitations of the work you have carried out and scope for further work.
Bibliography
Properly cite your references. This must conform to Harvard Style.
Presentation
Please give consideration to the readability of your project e.g. the formatting of tables, figures, equations and regression output. I would expect you to include the main figures/tables/equations etc. within the main body of the project. Additional figures and tables can be included in an appendix (which will not count towards the word count) but remember your marker will not necessarily look at the material in the appendix, so use it wisely!
Appendix
You must include a do-file of all the commands used in producing the work associated with your project. You can also use the appendix to include additional material, such as additional regressions run or regression output from EXCEL.
Preliminary Advice
Start Early
Think about some potential topics you might be interested in NOW. Try not to luxuriate in the comfort of having a deadline which at this stage is almost 4 months away.
· Be Realistic
Use bibliographic sources and online databases to obtain relevant articles. Be realistic in the number of references used (textbooks, journal articles, working papers, articles in the FT, Economist, etc). Avoid referencing tabloid newspapers.
· Make the Best Use of Previous Academic Literature
Use these references to identify approaches, both in terms of the estimation methods used but more specifically in the choice of variables. This will provide you with an idea of the most appropriate variables that have been used in previous studies.
· Effective and Efficient
Try to work effectively. It is often good practice to make a list of tasks to be carried out before sitting down at the computer – this will enable you to work more efficiently.
· Be Prepared
Be prepared to spend a long time with the data, both in terms of preparing the dataset and the statistical analysis (unfortunately this is inevitable). Try not to lose the original datafile just in case things go wrong! Also, you may have to compromise on your topic choice, or choice of variables, depending on data availability. Save your work regularly (and save to multiple locations)!
· Persevere
The skills you develop in this module WILL be useful!
Academic Integrity and Citation Consultations
How Librarians Can Help
For questions regarding bibliographies/works cited lists, librarians will:
Explain the general rules and logic of the citation format and teach students how to apply a required style to their papers.Point out important and unique elements of each citation style.Search for patterns of error in a bibliography or works cited list and explain the correct formatting when repeated errors are detected.Help students construct a citation for items that don’t fit into predetermined categories.
Provide samples of the style, manuals, or links to further information.
For questions regarding appropriate attribution, librarians will:
Explain the principles of academic integrity and plagiarism avoidance.Explain the general rules of attribution when quoting and paraphrasing (e.g. how and when to apply and format in-text notes vs. footnotes vs. endnotes).Search for patterns of inadequate attribution in a paper and explain the importance of academic integrity when evidence of plagiarism is detected.
Provide samples of appropriate attribution and manuals or links to further information.
Student Responsibilities
Students are expected to uphold UWS’s Community Standards. When seeking help from a librarian for citation and attribution, students should be aware of the following expectations:
Students are ultimately responsible for constructing their own bibliographies/works cited lists and for giving proper attribution to all sources consulted.Students must proofread their own work for accuracy and adherence to the correct citation style. Librarians cannot engage in line-by-line editing of a bibliography/works cited list or research paper.
Students must know what citation style they are required to use for each paper as this will change depending upon the subject and professor. Librarians cannot offer accurate advice without this information. If in doubt, verify with your professor before meeting with a librarian. This information is often found on your syllabus or assignment prompt Students must keep track of their own research and know what sources they are quoting or paraphrasing, as well as when another’s work is consulted in the
body of a research paper. Intentional plagiarism is the deliberate or unreasonably careless representation of another’s work as your own, either as a portion of your paper or as its entirety. This includes, but is not limited to, purchasing a paper that someone else wrote, using a friend’s paper, downloading a paper from the Internet, or knowingly aiding in another student’s intentional plagiarism. Intentional plagiarism represents academic misconduct, and I intend to fully pursue all instance
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