similar to: possible bug in vars package (predict.varest) ???

Hi I'm getting an error message with the roots() function in the vars package. Even the example in the help file comes up with an error: > data(Canada) > var.2c <- VAR(Canada, p = 2, type = "const") > roots(var.2c) Error in UseMethod("roots") : no applicable method for 'roots' applied to an object of class "varest" The error is odd, for

Folks, I have been using the VAR {vars} program to find a fit for the following bi-variate time series (subset): bivariateTS<-structure(c(0.950415958293559, 0.96077848972081, 0.964348957109053, 0.967852884998915, 0.967773510751625, 0.970342843688257, 0.97613937178359, 0.980118627997436, 0.987059493773907, 0.99536830931504, 1.00622672085718, 1.01198013845981, 1.01866618122606,

Dear All I am a biginner of R. I have difficulty with reading the code of a method. I am using the vars package to estimate a VAR model and I want to view the code of "predict" method for objects with class attribute "varest". I thougt I could just type the name "predict" without anything to display the code of the method as I often do with generic function.

I have been reviewing the arch test of vars package that is based on Engle's paper. R-metrics has this as a wants/todo item. I need an arch test and thought I might accomplish two things at once or at least try. The arch test requires a varest object and I am trying to write one that will use R-metrics arima, arch, Garch objects, or at least a vector. Also the arch function has the

Hello, I am running R version 2.4.1 on Debian. I am using some R functions which produce several graphics displayed successively with an interactive prompt between each graph. For example, the plot.varest (vars) function produces such output (one graph per time series on which the VAR model is fitted). Plots on GARCH models also produce such outputs. Moreover, the frontend I am using for R

Good day, I'm using a VAR model to forecast sales with some extra variables (google trends data). I have divided my dataset into a trainingset (weekly sales + vars in 2006 and 2007) and a holdout set (2008). It is unclear to me how I should predict the out-of-sample data, because using the predict() function in the vars package seems to estimate my google trends vars as well. However, I want

Hi all, I have a newbie question: If I have a function with the following documentation: ca.jo(x, type = c("eigen", "trace"), ecdet = c("none", "const", "trend"), K = 2, spec=c("longrun", "transitory"), season = NULL, dumvar = NULL) Let's take "type" as an example... if I omit this parameter when calling 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

To anyone who uses the Match() function in the Matching library... How do you go about deciding how many matches you will use? With my data, my standard errors generally get smaller if I use more matches. Speaking of standard errors, when correcting for heteroscedasticity, how many matches do you use (this is the Var.cal option). It seems to me that it might make sense to use the same number

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 All, My question is: how can I estimate VECM system with "unrestricted trend" (aka "case 5") option as a deterministic term? As far as I know, ca.jo in urca package allows for "restricted trend" only [vecm <- ca.jo(data, type = "trace"/"eigen", ecdet = "trend", K = n, spec = "transitory"/"longrun")].

hey there! The object 'cit' contains: > cit ##################################################### # Johansen-Procedure Unit Root / Cointegration Test # ##################################################### The value of the test statistic is: 5.3484 9.0681 10.6433 --------------- I want R to save the value 5.3484 in a new object. I am used to use the command x=cit[a] where a

Hi, I'm using package "vars" and I'm trying to extract the algorithm that function "predict" contained in that package in order to understand how does it work. When I type function "VAR" then all its algorithm appears in R, however if I try to do the same with "predict" nothing happens...Is there any possible way to extract the algorithm? Thanks a

Hello all, I have been searching thru all docs that I can find on wiki and such but can not get an answer. I am trying to collect a date from user input in the form of digits dialed from the phone to use in an agi script to do a database look up. I have tried to use "Get Data filename, timeout, maxdigits " in the agi script. In * console I get message saying playing filename but it

Is it possible using asterisk to allow someone to dial in and remotely change where their call is forwarded to? For example, I'm working from home so I want my calls to go to 555 1234, now I need to go out for a bit so I'd like to phone the office and using DTMF tell the asterisk PBX to now forward my calls to my cell phone 555 3456 Has anyone implimented anything like this? R.

I am fitting a reduced form VAR model using VAR in the vars library. I have several endogenous variables, and two exogenous variables. I would like to explore the effects of a shock to one of the exogenous variables on one of the endogenous variables. Using irf in the vars library only calculates the irf for the endogenous variables, this is obviously by design, is there some theoretical

Hello! I am using package "VAR". I've fitted my model: mymodel<-VAR(mydata,myp,type="const") I can extract the Log Liklihood for THE WHOLE MODEL: logLik(mymodel) How could I calculate (other than manually) the corresponding Akaike Information Criterion (AIC)? I tried AIC - but it does not take mymodel: AIC(mymodel) # numeric(0) Thank you! -- Dimitri Liakhovitski

Hello to everyone! I am working on my final year project about multivariate time series. There are three variables in the multivariate time series model. I have a few questions: 1. I used acf and pacf plot and find my variables are nonstationary. But in adf.test() and pp.test(), the data are stationary. why? 2.I use VAR to get a model. y is the matrix of data set and I have made a once

Hi all, The following code snippet isn''t working terribly well for me. I can get the Bias, StandardName and DaylightName, but everything else is goofed up. The alignment seems ok, but maybe I''ve missed something or maybe I have to do extra work to unpack the SYSTEMTIME structures. require ''windows/time'' include Windows::Time buf = 0.chr * 172 #

I have several older telephones with rotary dials that I would like to use a working museum pieces. I have everything working well except for those hard-coded codes that start with *. In the traditional phone world, dialing 11 in place of * works fine, ie, someone could dial 1172 in place of *72 and so forth. I'm thinking that a simple entry in extensions.conf ought to do the trick, but