search for: dumvar

...names(seasonal) <- seq(nrow(data.all) + 1, length = n.ahead) if (!is.null(Zdet)) { Zdet <- as.matrix(cbind(Zdet, seasonal)) } else { Zdet <- as.matrix(seasonal) } } if (!is.null(eval(object$call$exogen))) { if (is.null(dumvar)) { stop("\nNo matrix for dumvar supplied, but object varest contains exogenous variables.\n") } if (!all(colnames(dumvar) %in% colnames(data.all))) { stop("\nColumn names of dumvar do not coincide with exogen.\n") } if (!i...

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 function, what's going to be the default value for this parameter? Amazingly the documentation didn't say which one is the default... Thank you! [[alternative HTML version deleted]]

...trying to do it. I run the following code, that results in the following error: data(Canada) endoC <- Canada[1:72,1:3] exoC <- Canada[1:72,4] var.2c <- VAR(endoC, p = 2, season=12,exogen=exoC,type = "const") exoC_2 <- as.matrix(Canada[73:84,4]) predict(var.2c, n.ahead = 12,dumvar=exoC_2) Error: Error in as.matrix(cycle, nrow = season, ncol = season - 1) : unused argument(s) (nrow = 12, ncol = 11) from what I can guess, the predict.varest function is using as.matrix() and passing nrow and ncol, as.matrix() does not take those two parameters but matrix() does. Doe...

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 doing something wrong - what should I change? Any help very much appreciated! Ralph _________________________________________________________________ Need...

...l2 = lag(ts_Y,-2), ts_Y.l3 = lag(ts_Y,-3), ts_XGG.l1 = lag(ts_XGG,-1), ts_XGG.l2 = lag(ts_XGG,-2), ts_XGG.l3 = lag(ts_XGG,-3), ts_XGL.l1 = lag(ts_XGL,-1), ts_XGL.l2 = lag(ts_XGL,-2), ts_XGL.l3 = lag(ts_XGL,-3), const=1, trend=0.0001514194 ))); var.predict = predict(object=var_model, n.ahead=52, dumvar=holdout_matrix); ## Assess accuracy calc_mape (holdout_matrix$ts_Y, var.predict, islog=T, print=T) Some context: For my Master's thesis I'm using R to test the predictive power of web metrics (such as google trends data & pageviews) in sales forecasting. To properly assess this, I...

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