Data mining in econometric modelling Would I be "mining the data" in a time series analysis if I add more lags than theory suggests? For instance, for annual data analysis, it is recommended that two lags would be sufficient to capture the dynamic interactions among the variables and account for potential serial correlations. Similarly, in models that use quarterly data, using four lags is a norm. However, in many instances, there is a conflict between what theory says and what one could observe in actual data.

Just wondering if it amounts to "data mining" when I augment the model with more than 2 lags of annual data. Assume the lag order is selected using an information criterion.
The critical points here are the phrases "it is recommended that...", and "using four lags is a norm". "Recommended" and "norm" based on what? On a specific prevailing theoretical model, or on past experience with the specific kind of data? If it is the latter, you do not conflict with any economic theory, if you try something different.

This is also a good example of the need to arrive at a synthesis where the model is both theoretically supported and also statistically adequate (consult the works, especially the books, of prof. Aris Spanos on this important methodological matter).