The importance and application of Vector Error Correction Model (VECM)

The auto regressive category has three distinct models; let is clear our understanding of them using the table below.

Each model has its own strengths and applicability depending on the characteristics of the data and the problem statement. VAR is suitable for short-term dynamics, VECM for long-term equilibrium relationships, and ARDL for analyzing relationships in a single equation framework with mixed-order integrated variables.

Here, we will discuss anout VECM and when do we apply error correctionj process. VECM is an extension of the Vector Autoregression (VAR) model where variables are assumed to be stationary. We can call VECM as cointegrated VAR. Wikipedia has a clear definition of error correction model. Many commonly used time series (for example, in economics) appear to be stationary in first differences. Forecasts from such a model will still reflect cycles and seasonality found in the data. However, any long-run changes that the data in levels may include are ignored, making longer-term estimates incorrect. This prompted Sargan to create “error correction model” to retain level information.

VECM is useful when dealing with economic and financial data where variables are often non-stationary and exhibit long-run relationships. .