biascorrect: AR Bias Correction for short time series
In pedrognicolau/ARbiascorrect-v1: AR Bias Correction for short time series

Description Usage Arguments Value References Examples

Gives bias-corrected estimates and 95% confidence intervals for autoregressive coefficients of AR(1) and AR(2) processes, for sample sizes n=10,11, ..., 50.

biascorrect(
  phi = NULL,
  n = NULL,
  method = c("yw", "mle", "burg", "cmle"),
  order = NULL,
  x = NULL
)

`phi`	single value (AR1) or two-dimensional vector (AR2) containing the AR estimates subject to bias correction. Not required if time series `x` is used as input.
`n`	integer for the length of the time series. Needs to be between 10 and 50. Not required if time series `x` is used as input.
`method`	Character string specifying the method used to estimate the autoregressive coefficients. Needs to be either `mle`, `cmle`, `burg` or `yw`, specifying the exact MLE, the conditional MLE, Burg's method and the Yule-Walker solution, respectively.
`order`	order of the estimated autoregressive process. `x` is used as input.
`x`	The time series to be fitted by AR(1) or AR(2) in which `order` needs to be specified. The default estimation method is the exact MLE if none is specified.

A list with the following elements

phi.hat The original estimates of the AR coefficients
phi.correct The bias-corrected estimates
ci.hat The 95% confidence interval for the original estimates
ci.correct The 95% confidence interval for the bias-corrected estimates

Sørbye, S. H., Nicolau, P. G. & Rue, H. (2021). Finite sample properties of estimators for first and second order autoregressive processes.

# pre-computed estimates
biascorrect(phi=c(0.5,-0.2), n=15, method="mle")



# simulating series with ARIMA sim
series <- arima.sim(n = 20, list(ar = c(0.2, 0.5), sd = sqrt(0.04)))
biascorrect(x=series, order=2, method="mle")