Lag Identification for Vector Nonlinear Time Series

Exploratory methods for determining appropriate lagged variables in a vector nonlinear time series model are investigated. The rst is a multivariate extension of the R statistic considered by Granger and Lin (1994), which is based on an estimate of the mutual information criterion. The second method uses Kendall's and partial statistics for lag determination. Both methods provide nonlinear analogs of the autocorrelation and partial autocorrelation matrices for a vector time series. Simulation studies indicate the methods reliably identify appropriate lags for many types of vector nonlinear time series. For illustration, the methods are applied to set of annual temperature and tree ring measurements at Campito Mountain in California.