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What is an unbalanced panel dataset?
An unbalanced panel (e.g., the second dataset above) is a dataset in which at least one panel member is not observed every period. Therefore, if an unbalanced panel contains N panel members and T periods, then the following strict inequality holds for the number of observations (n) in the dataset: n < N×T.
What is the difference between Areg and Xtreg?
Edited to add: The difference between what -areg- and what -xtreg- are doing is that -areg- is counting all of the fixed effects against the regression’s degrees of freedom, whereas -xtreg- is not. This would be akin to “double-counting” these fixed effects, so -xtreg- is doing the right thing.
How to check unbalanced panel data, which approach?
It does not check whether any of the other variables in the data set contain missing values, which constitutes an unbalanced panel in the econometric sense. That said, you can still just use the xtreg command (or almost any other command of interest) in the usual way as already suggested by Carlo.
What can I do about unbalanced datasets?
In this last case, you can first try using a clustering technique or k-NN (k-nearest neighbors algorithm) to obtain a downsampled dataset. This dataset includes observations of every natural group of data inside the majority class.
Why are some models more susceptible to unbalanced data?
Besides, the problem is that models trained on unbalanced datasets often have poor results when they have to generalize (predict a class or classify unseen observations). Despite the algorithm you choose, some models will be more susceptible to unbalanced data than others. Ultimately, this means you will not end up with a good model.
What is the accuracy paradox of an unbalanced dataset?
In unbalanced datasets, the “ Accuracy Paradox ” is common. It occurs when you use the “accuracy” metric to learn the best model. Let’s continue with the example of fraud detection data. The algorithm will want to assign 99% of the observations to the majority class, as then the model’s accuracy will be above 90%.