anozman wrote:My procedure is based on the first scenario you mentioned: I use RATS to extract all the series from one big excel spreadsheet and do my modelling within this dataset as most of the input variables are the same. I extracted all data in the normal way and did not specify for each model its sample range (I was assuming each model should be able to pick up the maximum range (it is the range for the whole dataset) and if the series is shorter than this range, the program should start from the first available data point.
By default, RATS will indeed try use as many observations as it can for a given estimation. Observations for which the dependent variable or any of the regressors contain a missing value are dropped. Additional observations may be lost if the model includes lags of variables that contain missing values. The situation gets more complex for non-linear estimations, where you may lose observations due to issues like taking square roots of a negative, division by zero, and so on.
I'm not really sure what you meant by "independently" versus "run all the models together" in the first post, but if you are seeing differences in the sample between two estimations that you would expect to be identical, start with the information presented in the initial estimation output. Are the reported starting and ending periods the same, but with differing numbers of skipped/missing observations? Or do the start/end periods differ? Which of the two you are seeing should help you track down the source of the difference.
You might try using PRINT to display the residuals, the dependent variable, and the regressors for both instances (probably using explicit "start" and "end" parameters to ensure you view the data over the maximum possible range). See where the NAs (missing values) are showing up, and work backwards from the regression to trace the source of any differences.
If you can't figure out the source of the problem, we'd need to see at least the two program files in question, and it would help to have the data as well.
Regards,
Tom