Statistics and Data Modelling - Statistics Assignment Help

Assignment Task

Task brief

Assessment (Written empirical project)

Assessment 2 will be an individually written empirical project. You must select from one of the datasets listed on Blackboard and provide an analysis using this dataset. You are then to write up your analysis using Microsoft Word in a style format suggested (i.e. in the style of an empirical research paper). Your project must be no longer than 2,500 words. You must only choose ONE of the datasets to perform your analysis on. The datasets available are the same as those in Assessment 1. You may wish to/can extend upon the analysis you did for Assessment 1. However, this time you may wish to source your own supplementary data or datasets. This is up to you and relates to the question you wish to ask (and your broader interests). Your project must cover the following points in a way you feel is most appropriate for your project:

1. Statement of your research problem
2. Descriptive analysis
3. Inferential analysis
4. Discussion and conclusions

For those aiming for a higher classification in this task should in addition consider covering:

5. The robustness of your results in point 3 above.
6. Considerations of functional forms
7. How your research relates to the literature.

More detail of the above points is provided below.

1. Statement of your research problem

In this section you are required to consider a research problem. Having spent more time on the module, you may now wish to pursue the same research question as before, but this time with an adapted methodology. Or, you may simply want to pursue a completely different question altogether. Finally, you may wish to take this opportunity to `try out’ a potential research question that you may want to pursue in your dissertation. Once you have decided on a topic or research question, you can specify this as a hypothesis, or you may wish to simply state a question. These will usually be derived from some ‘research problem’ as defined by the current literature on this area and a theoretical framework.

2. Description of the data and Descriptive Analysis

For this section you must ensure you tell the audience about.

- The nature of the dataset (i.e. cross-sectional, time-series)

- The nature of the variables (i.e. continuous, ordinal, categorical)

This section will require you to do some work in R. Each of the datasets is different, so the descriptive analysis will also be very different. Think about what the most appropriate way to present your data is. You can provide a range of tables/plots/histograms to explain what the data shows and the composition of the data and its variables, as covered in Topic 2.

3. Inferential analysis

 In this section you may wish to use any of the techniques discussed in the module. This could include t-tests, simple linear regression, multiple linear regression and/or ARCH/GARCH models. You may also wish to use multiple methodologies. Remember, the purpose of inferential analysis is to build statistical evidence around your question and its answer. The more evidence you have, the more rounded your answer will be. You should think more strongly about how to present your results in an appropriate manner (e.g. in the format of regression tables).

4. Discussion and conclusions

Have you answered your question? What was the answer? What are the implications? Think about the question you have asked and the result you got. If you identify that X causes Y, why is this important to individuals, policy makers or other researchers.

5. Robustness of the result

When consider the robustness of the result you need to make sure that your analysis doesn’t suffer from heteroscedasticity or autocorrelation. Have you tested for violation of assumptions? What was the result? Did you need to correct for them? If so, how did you do this? You need to make sure your model(s) do not violate assumptions. It is not enough to just have a model, but the model needs to be robust and efficient; otherwise there potentially is no validity in your statistical approach.

6. Consideration of alternative functional forms

We have seen that the default linear regression may not always be the most helpful way to specify our regression models. This depends on the nature of our variables, our motivations and theoretical reasoning a-prior the research. Should you specify an alternative functional form? Some discussion on the models functional form should be considered and discussed. This naturally has implications for your results and implications.

7. How your research relates to the literature

Was the set of research questions and empirical model defined by present literature? Are you attempting to conduct a set of analysis on something nobody has done before? Do your results contribute evidence to interesting topics in finance, accounting and economics. Fulfilling this aspect of your assignment involves discussing other research which is similar to yours. What did other people find? Does this support your work or contradict it? Why might this be the case?

We will discuss in more detail throughout specifically designed topics how you should structure your work. But a common format may follow;

- Introduction – 300 words

- Brief literature review and theoretical framework - 700 words

- Empirical model(s) and specification – 600 words

- Results & Discussion – 600 words

- Conclusion – 300 words

This is only a very rough guide; you do not have to stick to this! More detail on the datasets you can choose from is provided on the following page;

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