So far I have used the aurocorregressive vectors, a technique of time series, this tells me that the existence of lags in time, but how could identify or quantify the lag, for example the response time to a stress event .
Hi Joaquin,
It appears as though you are a doctoral student in "agronomical engineering" in Columbia. I say this because the range of answers you receive may depend on the background of the scholars who respond. People from an economics background, like me, may have significantly different answers than those who are doing work in data mining, for example.
Often in econometrics we prefer to use theoretical deduction as the foundation to determine the model and then use empirical data to test it. Lags are almost always challenging because one could make a case for different lengths, especially with distributed lags. For example, some lags may show attractive results and be devoid of any theoretical foundation. If this would be a stretch at best, then scholars in economics might find it curious..
At the risk of over-simplification, my impression is that data mining might be used to allow a researcher to create a model, especially if one has not been developed and tested before. My point is that you may get really good suggestions from scholars in a variety of areas; however, as a doctoral student, it is probably wise to listen closely to the scholars in your area of agronomical engineering. Some scholars have strong and established preferences; others may be more eager to explore.
I hope this helps.
Rob
I have done is to place the variables in terms of vectors, then I make a relation between the hill the variables and determine a relation, that relationship will give me a distance, which would be associated with the response of a variable, later determination a statistical test to corroborate its significance, what do you think of this methodology?