Showing posts with label Elliot. Show all posts
Showing posts with label Elliot. Show all posts

Friday, August 30, 2019

Elliot, Rothenberg and Stock Unit Root Test

How to find Elliot, Rothenberg, and Stock Unit Root Test 

unit testing,unit test,unit tests,unit root test,unit root,jasmine unit test how to,unit tests in python,aeromotive phantom pump install and sending unit test,how to test,test driven development,stock vs aftermarket,unit testing phpstorm,dicky-fuller test

> data(nporg)
> gnp <- na.omit(nporg[, "gnp.r"])
> ers.gnp <- ur.ers(gnp, type="DF-GLS", model="const", lag.max=4)
> summary(ers.gnp)

###############################################
# Elliot, Rothenberg and Stock Unit Root Test #
###############################################

Test of type DF-GLS
detrending of series with intercept


Call:
lm(formula = dfgls.form, data = data.dfgls)

Residuals:
    Min      1Q  Median      3Q     Max
-39.767  -6.011   4.775  14.896  31.454

Coefficients:
             Estimate Std. Error t value
yd.lag        0.02559    0.01793   1.427
yd.diff.lag1  0.45630    0.14668   3.111
yd.diff.lag2  0.06223    0.15879   0.392
yd.diff.lag3 -0.02445    0.15965  -0.153
yd.diff.lag4 -0.10768    0.15110  -0.713
             Pr(>|t|) 
yd.lag        0.15962 
yd.diff.lag1  0.00303 **
yd.diff.lag2  0.69672 
yd.diff.lag3  0.87886 
yd.diff.lag4  0.47925 
---
Signif. codes:
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 16.49 on 52 degrees of freedom
Multiple R-squared:  0.3825, Adjusted R-squared:  0.3231
F-statistic: 6.442 on 5 and 52 DF,  p-value: 9.956e-05


Value of test-statistic is: 1.4268

Critical values of DF-GLS are:
                1pct  5pct 10pct
critical values -2.6 -1.95 -1.62
> sjd <- denmark[, c("LRM", "LRY", "IBO", "IDE")]
> sjd.vecm <- ca.jo(sjd, ecdet = "const", type="eigen", K=2, spec="longrun",
+                   season=4)
> HD1 <- matrix(c(1, -1, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 1), c(5,3))
> DA <- matrix(c(1,0,0,0, 0, 1, 0, 0, 0, 0, 0, 1), c(4,3))
> summary(ablrtest(sjd.vecm, H=HD1, A=DA, r=1))

######################

# Johansen-Procedure # 

######################

Estimation and testing under linear restrictions on alpha and beta

The VECM has been estimated subject to:
beta=H*phi and/or alpha=A*psi

     [,1] [,2] [,3]
[1,]    1    0    0
[2,]   -1    0    0
[3,]    0    1    0
[4,]    0   -1    0
[5,]    0    0    1


     [,1] [,2] [,3]
[1,]    1    0    0
[2,]    0    1    0
[3,]    0    0    0
[4,]    0    0    1

Eigenvalues of restricted VAR (lambda):
[1] 0.4100 0.0090 0.0053

The value of the likelihood ratio test statistic:
2.13 distributed as chi square with 2 df.
The p-value of the test statistic is: 0.35

Eigenvectors, normalised to first column
of the restricted VAR:

        [,1]
[1,]  1.0000
[2,] -1.0000
[3,]  5.9508
[4,] -5.9508
[5,] -6.2162

Weights W of the restricted VAR:

        [,1]
[1,] -0.1519
[2,]  0.0992
[3,]  0.0000
[4,]  0.0288

Black-Scholes formula-R

 Black-Scholes formula-R > BlackScholes <- function(TypeFlag = c("c", "p"), S, X, Time, r, b, sigma) { TypeFla...