similar to: Fitting VAR and doing Johansen's cointegration test in R

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

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

Hi, I would like to replace all the max values per row with "1" and all other values with "0". If there are two max values, then "0" for both. Example: from: 2 3 0 0 200 30 0 0 2 50 0 0 3 0 0 0 0 8 8 0 to: 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 Thanks! -- View this message in context:

? stato filtrato un testo allegato il cui set di caratteri non era indicato... Nome: non disponibile URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20101020/415e917c/attachment.pl>

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,

Hi, Please tell me the R codes for Engle Granger Test of cointegration. TIA Aditya

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

Dear all, S+Finmetrics has a number of very specilised functions. I am particularly interested in the estimation of cointegrated VARs (chapter 12 of Zivot and Wang). In this context the functions coint() and VECM() stand out. I looked at package "dse1", but found no comparable functionality. Are there any other packages you could point me to? In general, are there efforts for

Hi all, I got another VAR question here and really appreciate if somebody would help me out :) I have five time series, say A,B,C,D,E. My objective is to predict the series A using the rest, that is, B, C, D and E. A Vector Autoregression Model should work here. But first of all, I should select which series of B, C, D and E to be include in the VAR model, as well as the number of lags. I wonder

Hello! I'm using the blrtest() function in the urca package to test cointegration relationships. Unfortunately, the hypothesis (restrictions on beta) specifies the same restriction on all cointegration vectors. Is there any possibility to specify different restrictions on the cointegration vectors? Are there any other packages in R using cointegration tests? Thanks and best regards. Dennis

Hi - i am looking for a package with which I can perform panel cointegration tests. Old threads suggest plm and urca package, but I don't find suitable tests in these packs. Somebody knows more? best regards, Philipp -- View this message in context: http://r.789695.n4.nabble.com/panel-cointegration-tp4593443p4593443.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I am looking for low cost online education in statistics. I am thinking of taking online classes on time series analysis and cointegration, etc. Of course, if there are free video lectures, that would be great. However I couldn't find any free video lectures at upper-undergraduate and graduate level which formally going through the whole timeseries education... That's why I would

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

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 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 working on Johansen cointegration test, using urca and var package. in the selection of var, I have got following results. >VARselect(newd, lag.max = 10,type = "none") $selection AIC(n) HQ(n) SC(n) FPE(n) 6 6 6 5 $criteria 1 2 3 4 5 6 7 8 9 AIC(n) -3.818646e+01 -3.864064e+01

Dear All, I am looking for a cointegration relationship between Spot and Future Price of commodites. The problem i am facing follows: 1. After estimating by Engle-Grranger Method, i found that the residuals are stationary at their level I (o), which is required to fulfill the cointegration test. But the autocorrelation problem arises, as DW statistics is signficantly low 0.50-0.88 for various

Hi, I want to fit a mean and variance model jointly. For example I might want to fit an AR(2)-GARCH(1,1) model i.e. r_t = constant_term1 + b*r_t-1 + c*r_t-2 + a_t where a_t = sigma_t*epsilon_t where sigma^2_t = constant_term2 + p*sigma^2_t-1 + q*a^2_t-1 i.e. R estimates a constant_term1, b, c, constant_term2, p, q TIA Aditya

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

Hello all, I looking at package dse or vars or mAr I know how to simulate a VAR(p) process, my problem is that most of those processes are unstable (not weakly stationary). Do anybody know how to generate a random VAR (or VARMA even better) process that is weakly stationary? Thanks -- View this message in context: http://r.789695.n4.nabble.com/simulating-stable-VAR-process-tp4261177p4261177.html