The purpose of this assignment is to apply the Autoregressive Distributed Lag (ARDL) technique to analyze time series data for a listed company in Saudi Arabia (KSA).

Assignment 02: Time Series Analysis using ARDL Technique

 Objective:

The purpose of this assignment is to apply the Autoregressive Distributed Lag (ARDL) technique to analyze time series data for a listed company in Saudi Arabia (KSA). You will investigate the relationship between Return on Equity (ROE) as the dependent variable and selected independent variables, including Close price and two other variables of your choice. The assignment aims to assess your understanding of unit root tests, cointegration, short-term and long-term models, and diagnostic tests.

 Instructions:

Data Collection: Choose a KSA-listed company and collect time series data for the dependent variable (ROE), independent variables; Close price and any other relevant variable. Ensure that you have at least 35 observations.

 

Analysis:

a. Unit Root Test Table (2 Marks): Conduct unit root tests for all the variables in your dataset and provide a table summarizing the results.

 

b. Cointegration (Bound Test) Table (2 Marks): Apply the bound test for cointegration to check if there is a long-term relationship between ROE and the independent variables. Present the results in a table.

 

c. Short-term Model (2 Marks): Estimate a short-term ARDL model using appropriate lag lengths. Discuss the model’s results, coefficients, and statistical significance.

 

d. Long Run Model (2 Marks): Estimate a long-run ARDL model based on the cointegration results. Analyze the long-run relationships between ROE and the independent variables.

 

e. Diagnostic Test (2 Marks): Perform diagnostic tests to evaluate the goodness-of-fit and the validity of your models. Discuss any potential issues and suggest improvements if necessary.

 

Submission:

 

Prepare a clear and organized report that includes all the components mentioned above.

Make sure to provide interpretations and explanations for your findings.

Submit your assignment by the deadline, which is 7th November 2023.

Grading:

Your assignment will be graded based on the quality of your analysis, the correctness of your statistical tests, the clarity of your explanations, and the overall presentation of your findings. Each section mentioned above is allocated a specific number of marks.

 

If you have any questions or need clarification on any aspect of this assignment, please feel free to reach out for assistance.

 

Good luck with your analysis, and I look forward to reviewing your submission.

 

Note: Plagiarism and unauthorized collaboration are strictly prohibited. Please ensure that your work is original and properly referenced.