similar to: Modified Sims test

I mainly program in Common Lisp and use R for statistical analysis. While in R I miss the power and ease of use of Lisp, especially its many primitives such as find, member, cond, and (perhaps a bridge too far) loop. Has anyone created a package that includes R analogs to a subset of Lisp functions? Chris Elsaesser, PhD Principal Scientist, Machine Learning SPADAC Inc. 7921 Jones

I would like to try a likelihood ratio test in place of waldtest. Ideally I'd like to provide two glm models, the second a submodel of the first, in the style of lrt (http://www.pik-potsdam.de/~hrust/tools/farismahelp/lrt.html). [lrt takes farimsa objects] Does anyone know of such a likelihood ratio test? Chris Elsaesser, PhD Principal Scientist, Machine Learning SPADAC Inc. 7921

New to R; please excuse me if this is a dumb question. I tried to RTFM; didn't help. I want to do a series of regressions over the columns in a data.frame, systematically varying the response variable and the the terms; and not necessarily including all the non-response columns. In my case, the columns are time series. I don't know if that makes a difference; it does mean I have to call

Dear Achim, R Users, What am I doing wrong in this example ? a<-zoo(rnorm(100),order.by=1:100) b<-lag(a) regr<-na.exclude(merge(a,b)) plot(regr) grangertest(regr[,1],regr[,2],3) > a<-zoo(rnorm(100),order.by=1:100) > b<-lag(a) > regr<-na.exclude(merge(a,b)) > plot(regr) > grangertest(regr[,1],regr[,2],3) Error in solve(vc[ovar, ovar]) : subscript out of bounds

Hi useRs.. I cant figure out how to test for causality using causality() in vars package I have two datasets (A, B) and i want to test if A (Granger)cause B. How do I write the script? I dont understand ?causality. How do I get x to "contain" A and B. Further using the command VAR() to specify x, I dont either understand. Kind regards Tobias -- View this message in context:

Dear R Users, Can someone point me to a R package which will help me optimally choose a lag for Granger Causality testing ? Many thanks in advance, Tolga Generally, this communication is for informational purposes only and it is not intended as an offer or solicitation for the purchase or sale of any financial instrument or as an official confirmation of any transaction. In the event you are

Dear Community, this is my first programming in R and I am stuck with a problem. I have the following code which automatically calculates Granger causalities from a variable, say e.g. "bs" as below, to all other variables in the data frame: log.returns<-as.data.frame( lapply(daten, function(x) diff(log(ts(x))))) y1<-log.returns$bs y2<- log.returns[,!(names(log.returns) %in%

Dear Community, For my masters thesis I need to perform a multivariate granger causality test. I have found a code for bivariate testing on this page (http://www.econ.uiuc.edu/~econ472/granger.R.txt), which I think would not be useful for the multivariate case. Does anybody know a code for a multivariate granger causality test. Thank you in advance. Best Regards -- View this message in context:

Hello dudes. I'm developing VAR analysis based on suggestions made by Horváth in its paper Canonical Correlation Analysis and Wiener-Granger Causality Tests. That's the reason I'm looking for if there's any R package to develop Wiener - Granger Causality Test. Thanks a lot for your unvaluable help. Regards from Mexico [[alternative HTML version deleted]]

Dear R Users, The granger.test command in the MSBVAR package estimates all possible bivariate Granger causality tests for m variables. If one passes a data frame with 3 rows, it returns 6 granger tests in two rows, one for the F-statistic and another for the p-value. For example: > a<-rnorm(1:10) > b<-c(lag(a),rnorm(1)) > c<-c(lag(b),rnorm(1)) >

Dear Community, I am new to R and have a question concerning the causality () test in the vars package. I need to test whether, say, the variable y Granger causes the variable x, given z as a control variable. I estimated the VAR model as follows: >model<-VAR(cbind(x,y,z),p=2) Then I did the following: >causality(model, cause="y"). I thing this tests the Granger causality of

I need a package that can compute missing values of n-dimensional vectors for n > 2. This is a kind of interpolation, complicated in dimensions higher than 2. The idea is that I have a set of fully specified vectors (i.e., with no missing values) and I get a new vector that has one or more missing attributes; I need to fill in the missing values with values that are, based on the 'training

Hi All, I'm currently doing nightly full backups of /home. I'm not sure if a full back up is the best way to do backups. I know with my fileserver i do weekly full backup and daily incremental which would file on those type of files. I guess my question to the group is, how do you guys backup Maildir? whats the best way of doing it? -- Gabe Granger Pancentric results-driven

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

Hello to all- 1. When I run a regression which implements the augmented Dickey-Fuller test, I am confused about the names given to the regressors in the output. I understand what "xGE" stands for in a standard "lm" test involving an independent variable GE for instance, but if I lags and or differences are included in the model, what do the following "output" stand

Dear R Community, I am currently student at the Vienna University of Technology writing my Diploma thesis on causality in time series and doing some analyses of time series in R. I have the following questions: (1) Is there a function in R to estimate the PARTIAL spectral coherence of a multivariate time series? If yes, how does this work? Is there an test in R if the partial spectral

Dear All, I am looking for an R implementation of the convergent cross mapping method (see http://bit.ly/XN8OZX and http://www.uvm.edu/~cdanfort/csc-reading-group/sugihara-causality-science-2012.pdf ) The method is presented as an improvement over Granger causality ( http://bit.ly/XN8ydi ), but its implementation (involving shadows of multidimensional manifolds) must be quite some work...

Dear all, I am searching for a way to compute a test comparable to Chuang et al. ("Causality in Quantiles and Dynamic Stock Return-Volume Relations"). The aim of this test is to check wheter the coefficient of a quantile regression granger-causes Y in a quantile range. I have nearly computed everything but I am searching for an estimator of the density of the distribution at several

Dear all, I know that my question is somewhat special but I tried several times to solve the problems on my own but I am unfortunately not able to compute the following test statistic using the quantreg package. Well, here we go, I appreciate every little comment or help as I really do not know how to tell R what I want it to do^^ My situation is as follows: I have a data set containing a

Hi folks, I am trying to perform a Granger causality analysis with panel data. There are some packages around for panel data analysis and Granger causality. However, I have found neither a package for both panel data and Granger causality nor any R procedures (homogenous/heterogenous causality hypotheses, related tests such as Wald, unit root tests etc.). Of course, someone must have