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ECON3183: Time Series Data Analysis

Individual Assignment

This document provides questions and requirements for the Individual Assignment of ECON3183, which accounts for 30% of the total marks.

A statement regarding academic honesty and the stand of using generative AI tools in this course:

To  achieve  the   desired  learning  outcomes  for  this   course,  students  must  complete individual assignments,  a test,  and  a term  project that  involves  conducting  empirical investigations/studies related to economics or finance. Students are expected to obtain data from a reliable source, perform exploratory data analysis, propose a causal inference strategy with justification, conduct empirical studies, and  interpret the    results independently.

To ensure that students meet the intended learning outcomes for this course, generative AI tools are not allowed for any submissions (including drafts or final versions) unless otherwise  specified  in  the  assessment  instructions.  All  work   (including  assignment reports, test answers, the term project report, and Stata codes) must be the student’s own or adequately attributed to its source. The use of ChatGPT or other AI tools for CA is considered equivalent to receiving help from someone else. It raises concerns that the work is not the student’s own unless the instructor has provided specific instructions to the contrary.

Penalties for unacceptable AI use may include resubmitting the work, partial deduction of  marks,  or  receiving  zero  marks  for  the  corresponding  CA  component.  Turnitin’s ‘Similarity Check’ and ‘AI detector’ features will be used to monitor the use of AI tools in this course.

Deadline: by 2/10//22025

Submission Method:

a)    Please  submit  your  typing  assignment  report  in  one  single  PDF  file  to  Turnitin ‘Submission Link: Report’ via iSpace. The file name of your PDF submissions should have the following format: ECON3183_Assignment_Student ID_Name in Pinyin (e.g., ECON3183_Assignment_190000001_Text) and

b)    Save    your    data     and    do     file(s)    in     a    zip    file.     Name    your     zip    file    as ECON3183_Assignment_Student   ID_Name   in   Pinyin.   Then,   upload   your   file   to ‘Submission Link: Stata data and program’ via iSpace.

Assignment Guidelines:

Pages  11-12 of the Topic  1 Part I lecture notes present an example of cross-economy comparison of income levels using yearly nominal and real GDP per capita for several economies from 1970 to 2013. Visit World Bank Open Data (https://data.worldbank.org/) to

a)   Download annual nominal and real Gross National Income (GNI) per capita for the same economies to create similar graphs as shown on pages 11-12 of the Topic 1 Part I lecture notes using GNI per capita data (50%), explain why you chose your variable, and add your comments or observations (10%);

b)  Extend the sample period to 2024 to recreate the graphs (30%), and provide your comments and observations to compare and contrast your graphs with those on pages 11-12 of the Topic 1 Part I lecture notes (10%).

Present your graphs with your comments/observations in a written report.

You are required to prepare and submit Stata .do file(s) for this assignment with your assignment report. Your .do file(s) should be able to replicate the results you presented in the assignment report.

Requirements: Your Stata .do file should have the following elements

1)    Read data from an Excel file to Stata (5% out of 50%/30%);

2)    Label the essential variables (5% out of 50%/30%);

3)    Use the following Stata comments and their options to ensure that the presentation and  information  of  the  graphs  you  presented  are  as  close  as  possible  to  those illustrated in the lecture notes (hint: use ‘help [command_or_topic_name]’ to consult the Stata help system for the corresponding command if needed):

-     twoway line (10% out of 50%/30%)

-     graph save (10% out of 50%/30%)

-     graph combine (10% out of 50%/30%)

-     graph export (10% out of 50%/30%)

4)   Apply loops as you see fit (20% out of 50%/30%).

5)    Stata .do file presentation and readability (30% out of 50%/30%).