log likelihood

[dbyroo1]

Can anyone tell me how to interpret the log likelihood. I know they are supposed to be used to compare which model fits your data better.
I have estimates of:
Regression 1 -222.73
Regression 2 -223.54
Regression 3 -228.31

I want to comment on the models to justify dropping and including variables but I'm not sure if it is good to have a higher or lower negative number!

[EViews Esther]

When a model has more parameters, it will fit the model better. Therefore, it is required to balance how good you fit the data with a variety of models.

It is common statistical practice that likelihood functions are used in the comparison of models for the same data. However, I do not recommend to use the loglikelihoods in your model comparison.

There have been several proposals to address this issue.
1. EViews also provides the Akaike Information Criterion (AIC) or Schwarz Criterion (or BIC). The best model usually displays the lowest AIC/BIC.
2. The BMA addin provides the most likely models as well as the averaged quantities of interest over all possible models.

[Khatai]

İf Akaike Information Criterion (AIC) or Schwarz Criterion (or BIC) values are negative, we must choose the lowest value in absolute value to define the best fitted model?

[EViews Esther]

We will choose the model with the smallest value.