The Chow test for structural stability is a statistical procedure used to assess whether the error term in a regression model is exogenous, meaning it is not co...
The Chow test for structural stability is a statistical procedure used to assess whether the error term in a regression model is exogenous, meaning it is not correlated with the regressors. In other words, it tests the null hypothesis that the error term is exogenous against the alternative hypothesis that it is correlated.
The Chow test uses the following steps:
Calculate the residual sum of squares (RSS) for the model.
**Calculate the Jarque-Bera statistic, which measures the amount of serial correlation in the residuals.
Compute the p-value for the Jarque-Bera statistic.
If the p-value is less than the significance level (usually 0.05), then we reject the null hypothesis that the error term is exogenous and conclude that the model is not structurally stable.
If the p-value is greater than the significance level, then we fail to reject the null hypothesis and conclude that the model is structurally stable.
The Chow test is a powerful tool for assessing the structural stability of regression models. It is particularly useful when the regressors are highly correlated or when the sample size is relatively small