similar to: How to obtain cointegrated relationship from ca.jo in urca package?

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Dear R Core Team, I am using package {urca} to do cointegration and estimate ECM model, but I have the following two problems: (1) I use ca.jo() to do cointegration first and can get the cointegration rank, alpha and beta. The next step is to test some restrictions on beta with blrtest(),bh5lrtest(), and bh6lrtest(). But none of them can add restrictions on all the cointegration

Hi,I obtained the ect term from cajorls in urca. I found a result that would like to obtain some explanation here. My setting is that I have 2 variate time series. I use ca.jo to perform Johansen test. 1. I found that sometimes, in the case where the ca.jo test statistics suggest that I have 1 relationship. After I investigate the ect obtained from cajorls (with r=1), the ect series fails unit

Hi all R users, I'm trying to replicate the same results that are given in a published article after been granted the same data that the authors use. I'm having problems to determine the cointegration rank 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()

I can't figure out how to get the "summary" method in the URCA package to work. E.g. when I use the following code fragment in the help for the "ca.jo" function, it always tries to use the "summary" method from the "base" package, not the "urca" package. How do I force it use the "summary" method of the "urca" package?

Hello, I am using urca package to run cointegration. I would like to find the standard error in the (normalized, Johansen) cointegration relationship. How can I do it? As far as I know, The function "cajorls" in the "urca" package provides the normalized cointegrating relationships. Nevertheless, it does not provide the standard deviation of the coefficient for each

I am trying to run a conintegration analysis. I am a former user of S-Plus and understand the output of the coint and VECM output, but I am having trouble understanding the equivalent output in R. Here is what I ran > coint=ca.jo(data,constant=T,K=2,spec="longrun") > summary(coint) The first portion of the output that I did not understand [,1] [,2] [,3] y1

Dear R-list member, an update of package 'urca' has been uploaded to CRAN (Mirror: Austria). In the updated release unit root and cointegration tests encountered in applied econometric analysis are implemented. The package is written in 'pure' R and utilises S4 classes. In particular, the Johansen procedure with likelihood ratio tests for the inclusion of a linear trend,

Hello Anyone know how can I calculate the value of the beta parameter when I know the number of cointegrating relationships between two variables. I mean, I using the procedure: ca.jo I do the following: summary (ca.jo (UR [, c (2.52)], type = "trace" ECDET = "trend", K = 2, spec = "longrun")) given that there is a cointegration relationship as I can get the

Hello!   I estimate vector error correction model (vecm) model. I have only one cointegratio relationship. I write :   joh.vecm.rls <- cajorls(joh.vecm, r=1) The output estimation is : Call: lm(formula = substitute(form1), data = data.mat) Coefficients:                up.d            expl.d        upd.d           r.d      ect1      -1.34e-01   4.55e+02   6.91e+00   2.43e+03 constant 

Hi, I have a couple of questions about johansen's test, in general: 1. I was able to obtain error correction term (ect) from cajorls$rlm$model properly. According the my ca.jo object on 2-variate series, the test suggests that the integration rank is 1. Which means that my ect should be stationary. However, I did test stationariy on ect and it shows non-stationarity and my acf still shows

Hi - I am using polr. I can get a result from polr fit by calling result.plr <- polr(formula, data=mydata, method="probit"); However, from the 'result.plr', how can I access standard error of the estimated coefficients as well as the t statistics for each one of them? What I would like to do ultimately is to see which coefficients are not significant and try to refit the

Hi, I am trying to estimate a VECM without constant using the following code: data(finland) sjf <- finland sjf.reg<-ca.jo(sjf, type = c("eigen"), ecdet = c("none"), K = 2,spec=c("transitory"), season = NULL, dumvar = NULL) cajools(sjf.reg) While the cointegration test does not use a constant, it is used in the cajools which I do not want. I am sure I am

I'm tring the functions to check the cointegration of a matrix. I'm using **Phillips & Ouliaris Cointegration Test** The function in *tseries* package is **po.test** and **ca.po** in *urca* The results with **URCA** are: > ca.po(prices, demean='none') ######################################## # Phillips and Ouliaris Unit Root Test #

Hello guys, I'm trying to do a pairs trading cointegration analysis on two stocks (AXAP and AXANY), but I get an error that I don't understand... Here's my code: setwd("S:/Users/Alexis/Desktop/Essai") #chemin du dossier contenant les donn?es donnees <- read.csv("Data_R.csv", head=T, sep=";", stringsAsFactors=F) library(xts) dates <-

Dear R users; I'm using ca.jo for a VECM model. Is there a way that I can get sd/p-value to see whether coefficients estimated are statistical significant? Thank you Yours, Yihsu [[alternative HTML version deleted]]

Hi, I obtained all the coefficients that I need from polr. However, I'm wondering how I can obtain the standard error of each estimated coefficient? I saved the Hessian and do something like summary(polrObj), I don't see any standard error like when doing regression using lm. Any help would be really appreciated. Thank you! - adschai

Dear folk, I am stempting to estimate a vector error correction model using a seemingly fractionally integrated multivariate time series. The *fracdiff *package provides tools to estimate degree of fractional integration. But *fracdiff *can't help me to: 1. test equality of two degrees of fractional integration, say d1=d2? 2. estimate a multivariate cointegrating error correction model,

Hello, I tried to use urca package (R) for cointegration analysis. The data matrix to be investigated for cointegration contains 8 columns (variables). Both procedures, Phillips & Ouliaris test and Johansen's procedures give errors ("error in evaluating the argument 'object' in selecting a method for function 'summary'" respectiv "too many variables,

Dear R - helpers, I am using the urca package to estimate cointegration relations, and I would be really grateful if somebody could help me with this questions: After estimating the unrestriced VAR with "ca.jo" I would like to impose the rank restriction (for example rank = 1) and then obtain the restricted estimate of PI to be utilized to estimate the VECM model. Is it possible? It