Steps to Estimate an Error Correction Model (ECM)
Estimating an ECM involves several steps:
- Testing for Stationarity: Use unit root tests like ADF (Augmented Dickey-Fuller) to determine if the series are non-stationary.
- Testing for Cointegration: Apply tests like the Engle-Granger or Johansen cointegration test to check if the series are cointegrated.
- Estimating the Cointegration Equation: Use Ordinary Least Squares (OLS) to estimate the long-term relationship.
- Constructing the ECM: Incorporate the error correction term and estimate the parameters.
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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