I have a multivariate time series dataset including interacting biological and environmental variables (plus possibly some exogenous variables). Beside seasonality, there is no clear long-term trend in the data. My purpose is to see which variables are related to each other. Forecasting is not really looked for.
Being new to time-series analysis, I read several references. As far as I understand, Vector Autoregressive (VAR) model would be appropriate, but I don’t feel comfortable with seasonality and most examples I found concerned economics field (as often with time series analysis…) without seasonality.
What should I do with my seasonal data?
I considered deseasonalizing them – for example in R, I would use
decompose and then use the
$trend + $rand values to obtain a signal which appears pretty stationary (as judged per
Results of the VAR model are confusing me (a 1-lag model is selected while I would have intuitively expected more, and only coefficients for autoregression – and not for regression with other lagged variables - are significant).
Am I doing anything wrong, or should I conclude that my variables are not (linearly) related / my model is not the good one (subsidiary question: is there a non-linear equivalent to VAR?).
[Alternatively, I read I could probably use dummy seasonal variables, though I can’t figure out exactly how to implement it].
Step-by-step suggestions would be very appreciated, since details for experienced users might actually be informative to me (and R code snippets or links towards concrete examples are very welcome, of course).