I am trying to model annual tree nut production using climate predictors.
The nut data (dependent) is a binary timeseries (0,1 - representing unsuccessful and successful nut production), with one observation per year, and with 90 years of data and two missing years (88 onservations).
The independent variables are monthly climate variables, including months in previous years (for example, Temp.July.t, Temp.July.t-1)
I'm using R, and have an basic-intermediate knowledge of statistics.
My problem is that the dependent data has strong temporal autocorrelation (nut production cannot be successful two years running). I'm looking for a pointer towards a technique that will allow me to deal with the autocorrelation in the binary data and create a statistical model that allows me to investigate the relationship between nut production and climate.