27 Apr This is a econometrics empirical report that requires the knowledge of using STATA program, to create suitable graphs and critically analyze and interpret

This is a econometrics empirical report that requires the knowledge of using STATA program, to create suitable graphs and critically analyze and interpret the results. The word limit is 1200 words sharp. Please feel free to ask any questions.

FACULTY OF BUSINESS AND LAW

aCADEMIC YEAR 2023/24

Coursework Assessment Brief

Module Code: UMED8M-15-2

Module Title: Introductory Econometrics

Submission Deadline: Before 14:00 on 6/12/2023

Is eligible for 2 calendar day late submission window

Assessment Component: Coursework

Assessment Weighting: 50 per cent of total module mark

Marking and feedback deadline (20 working days after end of grace period): 19/01/2024

N.B. all times are 24-hour clock, current local time (at time of submission) in the UK

Module learning outcomes assessed by this task:

MO1, MO2, MO3, MO4, MO5 and MO6 as outlined in the module specification.

Assessment Instructions

The coursework is a 1,200-word empirical report (50%).

Your answers should be written in the form of a report of an empirical analysis – as though for publication in a journal. It is an exercise in correctly reporting and interpreting the results. At each stage, you should comment on the model you have estimated. Some examples of things that should be included are:

· Interpret the coefficients (do they have the signs you expected; are they of a reasonable magnitude, i.e. do they make sense? Remember to use the units of measurement in your answers)

· Test your coefficients to see if they are significantly different from zero at the 5% and/or 1% level of significance.

· Assess the goodness of fit of your model, using appropriate statistics, such as the (adjusted) R2 or the F-statistic.

Include the output from Stata as an appendix to your report. Include any graphs as an appendix or within the body of the report. Think carefully about the presentation of your work.

Marks will be awarded for clarity of discussion and comprehensive analysis but not for repetition so do not laboriously give details of the t-test for every coefficient etc.

Preparations

Each student is supposed to conduct their analysis based on their individual dataset. To obtain your individual dataset, please apply the following steps:

1. Download the dataset Understanding Society.dta , which you can find on Blackboard in the folder “Assignment”. It is a pooled sub-sample of the UK Household Longitudinal Study you should be familiar with from exercises in the computing sessions.

2. Open it in Stata. Do not modify it.

3. Run the following lines of code using your 8-digit student ID instead of the placeholder:

set seed student_ID

sample 1

Save the resulting individual dataset. It contains 28 variables and around 800 observations.

4. Familiarise yourself with your dataset and the included variables. More information can be found in the documentation materials available in the “Assignment” folder in Blackboard.

Task description

Using your individual dataset, you will investigate potential determinants of monthly gross wages ( paygu_dv) for UK workers. The data comprises details on individuals’ labour market activities as well as potentially relevant socio-demographic characteristics. A brief description of the main variables can be found in the table below. More details for all variables can be obtained from the documentation materials or by using the Understanding Society’s Variable search function.

a) Explore the data characteristics

Graph paygu_dv against age_dv, sex_dv, hiqual_dv, jbhrs and urban_dv. Choose graphs that are suitable considering the respective variable’s scale. Comment on patterns observed and likely model specifications for the relationships. Find any means, standard deviations, correlations etc. that are interesting and/or useful in the analysis, once again taking the variables’ scale into account.

b) Investigate potential wage determinants in a regression framework

Estimate a wage regression with paygu_dv as dependent variable, using age_dv, sex_dv, jbhrs, urban_dv and a high education indicator as explanatory variables. Generate the latter based on hiqual_dv, combining the categories “degree” and “other higher” into a high education group, and the other four categories into the reference group.

Review the regression along the lines indicated in the instructions to answer this question.

c) Find the best model

Experiment with different model specifications and find a “best model”. A selection of all variables in the data set can be used. To identify a plausible specification, you may wish to resort to economic theory and the existing literature. Please note that not all available variables are relevant determinants of wages.

Possibly, a non-linear functional form can be applied, for example using log-transformations, squared terms, or interactive terms. Model specification tests can be performed and evaluated. Give your arguments why this is the best model. Present only your favourite model in more detail.

Marking Criteria

This is an empirical report so marks will be allocated on the content and presentation of the whole entity rather than on each section. You may find inspiration on how to present your results from empirical research published in high-quality journals.

Students could direct their attention to the tasks a), b) and c) with the following weighting: a) 30%, b) 40% c) 30%.

Marks will be allocated on the basis of clear presentation of results and lucid explanation of the obtained results. Few marks shall be allocated simply for obtaining “correct” statistical results. However, a high weighting will be given in the allocation of marks to evidence where you show:

1. knowledge and understanding of relevant material from lectures and tutorials, along with an ability to make effective use of such material in doing the specified tasks.

2. an ability to explain clearly, concisely and accurately what you are doing.

3. thoughtfulness and good practice in developing alternative models.

4. an understanding of the role of economic considerations in formulating and evaluating econometric models.

5. an ability to present your work professionally.

Presenting test results etc. in tables and figures may be very useful and can substantially help your presentation. Tables and figures should be properly labelled. It is not recommended to cut and paste the output of computer programmes only.

Formative feedback and support during the module

Formative feedback provides opportunities to reflect on your ongoing work and preparation for your assignment. You will be introduced to the dataset in your teaching sessions. Formative feedback will be given on your results in class, which will help you when you are writing your statistics report.

Word Limit

The word limit for this coursework is 1,200 words.

In line with UWE Bristol’s Assessment Content Limit Policy (formerly the Word Count Policy), word count includes all text, including (but not limited to): the main body of text (including headings), all citations (both in and out of brackets), text boxes, quotes, lists.

What is NOT included in this word count:

· econometric results in tables,

· equations,

· the reference list,

· and appendices.

Formatting

Please use the following file format: Word. We cannot ensure that other formats are compatible with markers’ software and cannot guarantee to mark incorrect formats.

All work should be word processed in 12-point font Times New Roman or Arial.

The first page of your coursework must include:

· Your student number

· The module name and number

· Your word count

· The coursework title

Referencing and Assessment Offences

UWE Bristol’s UWE’s Assessment Offences Policy requires that you submit work that is entirely your own and reflects your own learning, so it is important to:

· Ensure you reference all sources used, using the UWE Harvard system and the guidance available on UWE’s Study Skills referencing pages.

· Avoid copying and pasting any work into this assessment, including your own previous assessments, work from other students or internet sources.

· Develop your own style, arguments, and wording, so avoid copying sources and changing individual words but keeping, essentially, the same sentences and/or structures from other sources.

· Never give your work to others who may copy it.

· If an individual assessment, develop your own work and preparation, and do not allow anyone to make amends on your work (including proof-readers, who may highlight issues but not edit the work).

When submitting your work, you will be required to confirm that the work is your own. Text-matching software and other methods are routinely used to check submissions against other submissions to the university and internet sources. Details of what constitutes plagiarism and how to avoid it can be found on UWE’s Study Skills pages about avoiding plagiarism.

Instructions for submission

You must submit your assignment before the stated deadline by electronic submission through Blackboard. Notification that the electronic submission portal is open for your assignment is displayed (usually two weeks before the submission date) in the Coursework tab in myUWE, the Coursework tab in Blackboard and via an announcement in the Blackboard course.

Please allow sufficient time to upload your assignment, as the system becomes busier and slower as the deadline approaches. Only your final upload will be counted. Ensure all your information is submitted at one attempt to avoid overwriting your intended submission. Always check and retain your receipts.

For full guidance on online submission through Blackboard, see UWE’s Academic Advice pages on Assignments.

Submissions of coursework by any other method (including a paper copy, on disk or by email) are NOT permissible for this module unless specifically agreed in advance of the submission date.

Before submitting your work, please ensure that:

· You have proofread you work thoroughly to ensure your work is presented appropriately.

· You have addressed all the required elements of the assessment.

· You have referenced in accordance with the guidance provided.

· You have addressed each of the marking criteria.

· The submission is in the correct format.

Further guidance and support

UWE Bristol offer a range of Assessment Support Options that you can explore through this link, and both Academic Support and Wellbeing Support are available.

For further information, please see the Academic Survival Guide.

Personal Circumstances

If you are experiencing difficulties in completing a piece of assessment on time due to unexpected circumstances (for example illness, accident, bereavement), seek advice from a Student Support Adviser at the earliest opportunity. Appointments can be made via an Information Point or online via the Student Support Pages.

Student Support Advisers can advise as to whether you should submit an application for ‘Personal Circumstances (PCs)’, how to do so and what evidence is required to support the application. Further details on PCs can be found on the Student Support Pages.

The module leader cannot grant personal circumstances or extensions.

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