# ar1: Estimation of an AR(1) model In Rfast: A Collection of Efficient and Extremely Fast R Functions

 Estimation of an AR(1) model R Documentation

## Estimation of an AR(1) model

### Description

Estimation of an AR(1) model.

### Usage

ar1(y, method = "cmle")
colar1(y, method = "cmle")


### Arguments

 y For the case of ar1 this is a vector of time series. For the case of colar1 this is a matrix where weach column represents a time series. method This can be either "cmle" for conditional maximum likelihood or "yw" for the Yule-Walker equations.

### Details

Instead of the classical MLE for the AR(1) model which requires numerical optimsation (Newton-Raphson for example) we estimate the parameters of the AR(1) model using conditional maximum likelihood. This procedure is described in Chapter 17 in Lee (2006). In some, it assumes that the first observation is deterministic and hence conditioning on that observation, there is a closed form solution for the parameters. The second alternative is to use the method of moments and hence the Yule-Walker equations.

### Value

 param For the case of ar1 this is a vector with three elements, the constant term, the \phi term (lag coefficient) and the variance. For the case of colar1 this is a matrix with three columns, eahc of which carries the same aforementioned elements.

Michail Tsagris

### References

http://econ.nsysu.edu.tw/ezfiles/124/1124/img/Chapter17_MaximumLikelihoodEstimation.pdf

 rm.lines, varcomps.mle, rm.anovas 

### Examples

y <- as.vector(lh)
ar1(y)
ar(y, FALSE, 1, "ols")

ar1(y, method = "yw")
ar(y, FALSE, 1, "yw")

a1 <- colar1(cbind(y, y) )
b1 <- colar1(cbind(y, y), method = "yw")


Rfast documentation built on Nov. 9, 2023, 5:06 p.m.