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You need to select and complete ONE case study from Category A and ONE exercise from Category B. Please read through the options carefully before making your selection.
A. CASE STUDIES (Select ONE):
Option 1:
• Focus on cointegration and ECM techniques
• Excellent for those interested in macroeconomic relationships
OR
Option 2:
• Focus on VECM and dynamic interactions
• Ideal for those interested in financial markets
Selection Criteria for Case Studies:
• Consider your research interests
• Assess your comfort level with the required techniques
• Think about potential applications to your own research
B. GUIDED EXERCISES (Select ONE):
Option 1:
• Focus on bilateral exchange rate dynamics
• Suitable for those interested in international finance
OR
Option 2:
• Focus on market interactions and causality
• Perfect for those interested in stock market analysis
Selection Criteria for Exercises:
• Choose based on your career goals
• Think about practical applications
Important Notes:
1. You must select ONE case study and ONE exercise (total of TWO assignments)
2. Clearly indicate your selections in your submission
3. Follow the detailed steps provided for each assignment
4. Use the same structured approach even if you modify the variables or time period
5. Consult with your lecturer if you need guidance in making your selection
A. Case studies applying cointegration and ECMs
Case Study 1: The long-run relationship between consumption and income
Objective: Investigate the presence of a long-run equilibrium relationship between consumption and income using cointegration techniques and estimate an error correction model to capture the short-run dynamics.
Data: Quarterly data on real personal consumption expenditures (CONS) and real disposable personal income (INCOME) for the United States from 1960Q1 to 2020Q4.
Steps:
1. Download the data from a reliable source (e.g., Federal Reserve Economic Data, or FRED) and import it into EViews.
2. Plot the time series of CONS and INCOME to visualize their trends and potential cointegration.
3. Conduct unit root tests (e.g., ADF, PP) to determine the order of integration of the variables.
4. If both variables are I(1), proceed with the Johansen cointegration test to determine the number of cointegrating relations.
5. If cointegration is found, estimate the long-run equation using FMOLS or DOLS.
6. Estimate an error correction model to capture the short-run dynamics and the speed of adjustment to deviations from the long-run equilibrium.
7. Interpret the results and discuss their economic implications.
Case Study 2: The term structure of interest rates
Objective: Examine the cointegration relationship between short-term and long-term interest rates and analyze the dynamic interactions using a VECM.
Data: Monthly data on the 3-month Treasury bill rate (TB3MS) and the 10-year Treasury note
yield (GS10) for the United States from 1960M01 to 2020M12.
Steps:
1. Download the data from a reliable source (e.g., FRED) and import it into EViews.
2. Plot the time series of TB3MS and GS10 to visualize their trends and potential cointegration.
3. Conduct unit root tests (e.g., ADF, PP) to determine the order of integration of the variables.
4. If both variables are I(1), proceed with the Johansen cointegration test to determine the number of cointegrating relations.
5. If cointegration is found, estimate a VECM to capture the long-run equilibrium and short-run dynamics.
6. Generate impulse response functions and forecast error variance decompositions to analyze the dynamic interactions between the short-term and long-term interest rates.
7. Interpret the results and discuss their implications for monetary policy and the term structure of interest rates.
B. Guided exercises
Exercise 1: Cointegration and ECM for exchange rates
Objective: Test for cointegration between the exchange rates of two currencies and estimate an error correction model to capture the short-run dynamics.
Data: Daily exchange rates for the Euro (EUR) and the British Pound (GBP) against the US
Dollar (USD) from January 1, 2010, to December 31, 2020.
Steps:
1. Download the data from a reliable source (e.g., Bloomberg, Yahoo Finance) and import it into EViews.
2. Plot the time series of the exchange rates to visualize their trends and potential cointegration.
3. Conduct unit root tests (e.g., ADF, PP) to determine the order of integration of the variables.
4. If both variables are I(1), proceed with the Engle-Granger two-step cointegration test.
5. If cointegration is found, estimate the long-run equation using OLS and save the residuals.
6. Test the residuals for stationarity using the ADF test to confirm cointegration.
7. Estimate an error correction model using the saved residuals as the error correction term.
8. Interpret the results and discuss their implications for the foreign exchange market.
Exercise 2: VAR and Granger causality for stock market indices
Objective: Estimate a VAR model for two stock market indices and test for Granger causality between them.
Data: Daily closing prices for the S&P 500 (SPX) and the Dow Jones Industrial Average (DJIA) from January 1, 2000, to December 31, 2020.
Steps:
1. Download the data from a reliable source (e.g., Yahoo Finance) and import it into EViews.
2. Convert the price series to log returns to achieve stationarity.
3. Conduct unit root tests (e.g., ADF, PP) to confirm the stationarity of the log return series.
4. Estimate a VAR model with the log returns of SPX and DJIA, choosing the appropriate lag length based on information criteria (e.g., AIC, SC).
5. Generate impulse response functions and forecast error variance decompositions to analyze the dynamic interactions between the two stock market indices.
6. Conduct Granger causality tests to determine if one index helps predict the other.
7. Interpret the results and discuss their implications for portfolio management and market efficiency.