search for: 5pct

...1 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 Residual standard error: 0.9526 on 109 degrees of freedom Multiple R-squared: 0.2514, Adjusted R-squared: 0.2171 F-statistic: 7.321 on 5 and 109 DF, p-value: 5.989e-06 Value of test-statistic is: -2.4746 3.0877 Critical values for test statistics: 1pct 5pct 10pct tau2 -3.46 -2.88 -2.57 phi1 6.52 4.63 3.81 > CPILD4UR<-ur.df(x1$CPILD4[5:nr1], type ="drift", lags=12, selectlags ="BIC") > summary(CPILD4UR) ############################################### # Augmented Dickey-Fuller Test Unit Root Test # #####################...

...nk of my data set using the Johnasen's trace test. This trace test is already programmed in the package ur.ca and can be found in the function ca.jo(). After I run the ca.jo() function on my data set I get the following output: Values of teststatistic and critical values of test: test 10pct 5pct 1pct r<=10 9.71 7.52 9.24 12.97 r<=9 24.18 17.85 19.96 24.6 r<=8 45.16 32 34.91 41.07 r<=7 74.12 49.65 53.12 60.16 r<=6 113.96 71.86 76.07 84.45 r<=5 158.58 97.18 102.14 111.01 r<=4 207.95 126.58 131.7 143.09 r<=3 278.29 159.48 165.58 177.2 r<=2 361.34 196.37 202.92 215.7...

...)) cvals <- rbind( cval.tau3[rowselec, ], cval.phi2[rowselec, ], cval.phi3[rowselec, ]) testnames <- c('tau3', 'phi2', 'phi3') } colnames(cvals) <- c("1pct", "5pct", "10pct") rownames(cvals) <- testnames #new("ur.df", y=y, model=type, cval=cvals, lags=lag, teststat=teststat, testreg=testreg, res=res, test.name="Augmented Dickey-FullerTest") } test <- ur.df(y, type = "none", lags = 1)

.... Then I can conclude that x is mean-reverting. Am I correct? 2/ I would like to see critical values also, so I tried with ur.df > summary(ur.df(x, "trend", lag=0)) <snip> Value of test-statistic is: -31.8629 338.4156 507.6231 Critical values for test statistics: 1pct 5pct 10pct tau3 -3.96 -3.41 -3.12 phi2 6.09 4.68 4.03 phi3 8.27 6.25 5.34 Here if I understand correctly, as my first critical value is significantly less than the 1% critical value I reject the null hypothesis that x has a unit root, so x is stationary and then mean reverting. Thanks, -Arnaud

...sidual standard error: 1.73 on 749 degrees of freedom Multiple R-squared: 0.9503, Adjusted R-squared: 0.9502 F-statistic: 1.431e+04 on 1 and 749 DF, p-value: < 2.2e-16 Value of test-statistic is: 12.9648 Critical values of Pu are: 10pct 5pct 1pct critical values 20.3933 25.9711 38.3413 The result with TSERIES are: > po.test(prices, demean=FALSE) Phillips-Ouliaris Cointegration Test data: prices Phillips-Ouliaris standard = -25.6421, Truncation lag parameter = 7, p-value = 0.01 War...

...tt 3.459 NA NA NA Residual standard error: NaN on 0 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: NaN F-statistic: NaN on 2 and 0 DF, p-value: NA Value of test-statistic is: NaN NaN NaN Critical values for test statistics: 1pct 5pct 10pct tau3 -4.38 -3.60 -3.24 phi2 8.21 5.68 4.67 phi3 10.61 7.24 5.91 " Can anyone tell me what is wrong with the codes???The code used is given below. Thanx t=1:length(x) plot(t,x) trend = lm(x~t) abline(lm(x~t)) summary(trend) library(urca) x = ts(x, start=1, end = length(x), frequen...