ar1: Estimation of an AR(1) model

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/regression_models.R

Description

Estimation of an AR(1) model.

Usage

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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 φ 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.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <mtsagris@uoc.gr> and Manos Papadakis <papadakm95@gmail.com>.

References

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

See Also

rm.lines, varcomps.mle, rm.anovas

Examples

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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")

Example output

Loading required package: Rcpp
Loading required package: RcppZiggurat
 constant       phi     sigma 
0.7859809 0.6710498 0.2039206 

Call:
ar(x = y, aic = FALSE, order.max = 1, method = "ols")

Coefficients:
    1  
0.586  

Intercept: 0.006234 (0.06551) 

Order selected 1  sigma^2 estimated as  0.2016
     mean       phi     sigma 
2.4000000 0.5755245 0.2079007 

Call:
ar(x = y, aic = FALSE, order.max = 1, method = "yw")

Coefficients:
     1  
0.5755  

Order selected 1  sigma^2 estimated as  0.2079

Rfast documentation built on May 18, 2021, 1:07 a.m.