AR(m) model
linear(x, m, d=1, steps=d, series, include = c( "const", "trend","none", "both"), type=c("level", "diff", "ADF"), warn_root=TRUE)
A nlar object, linear subclass.
nlar
linear
time series
embedding dimension, time delay, forecasting steps
time series name (optional)
Type of deterministic regressors to include
Whether the variable is taken is level, difference or a mix (diff y= y-1, diff lags) as in the ADF test
Whether to check (and warn) for roots outside the unit circle?
Antonio, Fabio Di Narzo
AR(m) model: $$ x_{t+s} = \phi_0 + \phi_1 x_t + \phi_2 x_{t-d} + \dots + \phi_m x_{t - (m-1)d} + \epsilon_{t+s}$$
nlar for fitting this and other models to time series data
#fit an AR(2) model mod.linear <- linear(log(lynx), m=2) mod.linear summary(mod.linear)
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