Convert an object to class 'arx'
as.arx.lm.RdThe function as.arx is a generic function and its methods returns an object of class arx.
Usage
as.arx(object, ...)
##S3 method for objects of class 'lm':
# S3 method for class 'lm'
as.arx(object, ...)Arguments
- object
object of class
lm- ...
arguments passed on to and from other methods
Value
Object of class arx
Author
Genaro Sucarrat http://www.sucarrat.net/
Examples
##generate some data:
set.seed(123) #for reproducibility
y <- rnorm(30) #generate Y
x <- matrix(rnorm(30*10), 30, 10) #create matrix of Xs
##typical situation:
mymodel <- lm(y ~ x)
as.arx(mymodel)
#>
#> Date: Sat Jul 27 15:29:46 2024
#> Dependent var.: y
#> Method: Ordinary Least Squares (OLS)
#> Variance-Covariance: Ordinary
#> No. of observations (mean eq.): 30
#> Sample: 1 to 30
#>
#> Mean equation:
#>
#> coef std.error t-stat p-value
#> mconst 0.27005590 0.21026283 1.2844 0.21445
#> x1 -0.61303927 0.28160750 -2.1769 0.04230 *
#> x2 0.13398941 0.22947573 0.5839 0.56616
#> x3 0.30619954 0.21734292 1.4088 0.17504
#> x4 -0.00018761 0.19034597 -0.0010 0.99922
#> x5 0.16595175 0.20992401 0.7905 0.43897
#> x6 -0.16893171 0.21399989 -0.7894 0.43962
#> x7 0.51949160 0.22562893 2.3024 0.03279 *
#> x8 0.32756857 0.20626559 1.5881 0.12877
#> x9 -0.51817835 0.24477483 -2.1170 0.04768 *
#> x10 -0.01454824 0.19954250 -0.0729 0.94264
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Diagnostics and fit:
#>
#> Chi-sq df p-value
#> Ljung-Box AR(1) 0.06042745 1 0.8058
#> Ljung-Box ARCH(1) 0.00067615 1 0.9793
#>
#> SE of regression 0.93728
#> R-squared 0.40197
#> Log-lik.(n=30) -35.12486
##use hetero-robust vcov:
as.arx(mymodel, vcov.type="white")
#>
#> Date: Sat Jul 27 15:29:46 2024
#> Dependent var.: y
#> Method: Ordinary Least Squares (OLS)
#> Variance-Covariance: White (1980)
#> No. of observations (mean eq.): 30
#> Sample: 1 to 30
#>
#> Mean equation:
#>
#> coef std.error t-stat p-value
#> mconst 0.27005590 0.13837697 1.9516 0.06589 .
#> x1 -0.61303927 0.27212423 -2.2528 0.03629 *
#> x2 0.13398941 0.20116413 0.6661 0.51337
#> x3 0.30619954 0.22901348 1.3370 0.19700
#> x4 -0.00018761 0.14658909 -0.0013 0.99899
#> x5 0.16595175 0.14962022 1.1092 0.28121
#> x6 -0.16893171 0.14592354 -1.1577 0.26134
#> x7 0.51949160 0.19021354 2.7311 0.01327 *
#> x8 0.32756857 0.16763602 1.9540 0.06558 .
#> x9 -0.51817835 0.22303637 -2.3233 0.03141 *
#> x10 -0.01454824 0.11859578 -0.1227 0.90366
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Diagnostics and fit:
#>
#> Chi-sq df p-value
#> Ljung-Box AR(1) 0.06042745 1 0.8058
#> Ljung-Box ARCH(1) 0.00067615 1 0.9793
#>
#> SE of regression 0.93728
#> R-squared 0.40197
#> Log-lik.(n=30) -35.12486
##add ar-dynamics:
as.arx(mymodel, ar=1:2)
#>
#> Date: Sat Jul 27 15:29:46 2024
#> Dependent var.: y
#> Method: Ordinary Least Squares (OLS)
#> Variance-Covariance: Ordinary
#> No. of observations (mean eq.): 28
#> Sample: 3 to 30
#>
#> Mean equation:
#>
#> coef std.error t-stat p-value
#> mconst 0.312270 0.241746 1.2917 0.21600
#> ar1 -0.167514 0.340202 -0.4924 0.62957
#> ar2 -0.199858 0.266832 -0.7490 0.46544
#> x1 -0.592040 0.313247 -1.8900 0.07824 .
#> x2 0.080545 0.318537 0.2529 0.80381
#> x3 0.336700 0.243410 1.3833 0.18683
#> x4 0.087082 0.252641 0.3447 0.73511
#> x5 0.180548 0.240054 0.7521 0.46362
#> x6 -0.153325 0.269165 -0.5696 0.57736
#> x7 0.497405 0.251823 1.9752 0.06694 .
#> x8 0.282720 0.231063 1.2236 0.23999
#> x9 -0.515726 0.337073 -1.5300 0.14683
#> x10 -0.181715 0.291814 -0.6227 0.54283
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Diagnostics and fit:
#>
#> Chi-sq df p-value
#> Ljung-Box AR(3) 1.25213112 3 0.7405
#> Ljung-Box ARCH(1) 0.00085579 1 0.9767
#>
#> SE of regression 1.01385
#> R-squared 0.44128
#> Log-lik.(n=28) -33.61550
##add log-variance specification:
as.arx(mymodel, arch=1:2)
#>
#> Date: Sat Jul 27 15:29:46 2024
#> Dependent var.: y
#> Method: Ordinary Least Squares (OLS)
#> Variance-Covariance: Ordinary
#> No. of observations (mean eq.): 30
#> Sample: 1 to 30
#>
#> Mean equation:
#>
#> coef std.error t-stat p-value
#> mconst 0.27005590 0.21026283 1.2844 0.21445
#> x1 -0.61303927 0.28160750 -2.1769 0.04230 *
#> x2 0.13398941 0.22947573 0.5839 0.56616
#> x3 0.30619954 0.21734292 1.4088 0.17504
#> x4 -0.00018761 0.19034597 -0.0010 0.99922
#> x5 0.16595175 0.20992401 0.7905 0.43897
#> x6 -0.16893171 0.21399989 -0.7894 0.43962
#> x7 0.51949160 0.22562893 2.3024 0.03279 *
#> x8 0.32756857 0.20626559 1.5881 0.12877
#> x9 -0.51817835 0.24477483 -2.1170 0.04768 *
#> x10 -0.01454824 0.19954250 -0.0729 0.94264
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Log-variance equation:
#>
#> coef std.error t-stat p-value
#> vconst -1.86806 0.66211 7.9601 0.004782 **
#> arch1 -0.29450 0.20609 -1.4290 0.165375
#> arch2 -0.38209 0.20848 -1.8327 0.078778 .
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Diagnostics and fit:
#>
#> Chi-sq df p-value
#> Ljung-Box AR(1) 0.027496 1 0.8683
#> Ljung-Box ARCH(3) 0.574734 3 0.9022
#>
#> SE of regression 0.93728
#> R-squared 0.40197
#> Log-lik.(n=28) -30.52120