Practical Application and Use Cases of ECM
Example: Stock Prices and Market Index
Consider modeling the relationship between a stock’s price and a market index. If both series are non-stationary but cointegrated, an ECM can be used to capture the short-term adjustments and long-term equilibrium relationship.
- Test for Stationarity: Use ADF tests to confirm that both the stock price and market index are I(1).
- Cointegration Test: Perform a cointegration test to verify a long-run relationship.
- Estimate Long-Run Relationship: Regress the stock price on the market index and save the residuals.
- Estimate ECM: Regress the differenced stock price on the differenced market index and include the lagged residuals from the long-run regression.
Error Correction Model (ECM): A Comprehensive Guide
An Error Correction Model (ECM) is a powerful econometric tool used to model the relationship between non-stationary time series variables that are cointegrated. Cointegration implies that while individual time series may be non-stationary, a linear combination of them is stationary, indicating a long-run equilibrium relationship. ECMs are particularly useful for capturing both short-term dynamics and long-term equilibrium adjustments between variables.
Table of Content
- What is Error Correction Model (ECM)?
- How ECMs Manage Non-Stationary Data?
- 1. Understanding Non-Stationarity and Cointegration
- 2. Engle-Granger Two-Step Procedure
- 3. Model Specification
- 4. Handling Mixed Integration Orders
- Steps to Estimate an Error Correction Model (ECM)
- Interpreting Error Correction Models: Key Components and Their Significance
- Practical Application and Use Cases of ECM
- Advantages and Disadvantages of ECM
- Key Differences Between ECM and Other Time Series Models
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