如何判断Sawa's Bavesian Information Criteria

Sawa's Bayesian Information Criteria (BIC) is a statistical method for model selection that balances the goodness of fit of a model with its complexity. It is commonly used in the field of econometrics.

To calculate Sawa's BIC, you first need to estimate the parameters of the model using maximum likelihood estimation. Then, you calculate the BIC value using the following formula:

BIC = -2 * log-likelihood + k * log(n)

where log-likelihood is the log-likelihood of the estimated model, k is the number of parameters in the model, and n is the number of observations.

To judge the goodness of fit of a model using Sawa's BIC, you would compare the BIC values of different models and choose the model with the lowest BIC value. The lower the BIC value, the better the model fits the data while taking into account model complexity.