| Title: | Minimum Distance Estimation in Autoregressive Model |
| Version: | 1.0 |
| Description: | Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model. |
| Depends: | R (≥ 3.2.2) |
| License: | GPL-2 |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2015-09-14 05:48:40 UTC; Jason |
| Author: | Jiwoong Kim [aut, cre] |
| Maintainer: | Jiwoong Kim <kimjiwo2@stt.msu.edu> |
| Repository: | CRAN |
| Date/Publication: | 2015-09-14 09:12:45 |
Performs minimum distance estimation in autoregressive model
Description
Performs minimum distance estimation in autoregressive model
Usage
ARMDE(X, AR_Order)
Arguments
X |
: vector of n observed value |
AR_Order |
: oder of the autoregressive model |
Value
returns minimum distance estimators of the parameter in the autoregressive model
References
[1] Koul, H. L (1985). Minimum distance estimation in linear regression with unknown error distributions. Statist. Probab. Lett., 3 1-8.
[2] Koul, H. L (1986). Minimum distance estimation and goodness-of-fit tests in first-order autoregression. Ann. Statist., 14 1194-1213.
[3] Koul, H. L (2002). Weighted empirical process in nonlinear dynamic models. Springer, Berlin, Vol. 166
See Also
LRMDE
Examples
X <- rnorm(10, mean=0, sd=1)
AR_Order <- 2
rhohat<-ARMDE(X,AR_Order)