search for: xregs

Hello all, I use arima to fit the model with fit <- arima(y, order = c(1,0,1), xreg = list.indep, include.mean = TRUE) and would like to use predict() to forecast: chn.forecast <- rep(0,times=num.record) chn.forecast[1] <- y[1] for (j in 2:num.record){ indep <- c(aa=chn.forecast[j-1], list.indep[j,2:num.indep]) # this is the newxreg in the

Hi, I am trying to understand exactly what xreg does in arima. The documentation for xreg says:"xreg Optionally, a vector or matrix of external regressors, which must have the same number of rows as x." What does this mean with regard to the action of xreg in arima? Apparently somehow xreg made the following two arima fit equivalent in R: arima(x, order=c(1,1,1), xreg=1:length(x)) is

When I run predict.Arima in my code, I get warnings like: Warning message: In cbind(intercept = rep(1, n), xreg) : number of rows of result is not a multiple of vector length (arg 1) I think this is because I'm not running predict.Arima in the same environment that I did the fit, so the data object used in the fit is no longer present. Looking at the predict.Arima source,

Dear R-help-archive.. I am trying to figure out how to make arima prediction when I have a process involving multivariate time series input, and one output time series (output is to be predicted) .. (thus strictly speaking its an ARMAX process). I know that the arima function of R was not designed to handle multivariate analysis (there is dse but it doesnt handle arma multivariate analysis, only

I am using arima to develop a time series regression model, I am using arima b/c I have autocorrelated errors. Several of my independent variables are categorical and I have coded them as factors . When I run ARIMA I don't get any warning or error message, but I do not seem to get estimates for all the levels of the factor. Can/how does ARIMA handle factors in xreg? here is some example

Thanks a lot to everybody that helped me out with this. Conclusions: (1) In order to edit arima in R: >fix(arima) or alternatively: >arima<-edit(arima) (2) This is not contained in the "Introduction to R" manual. (3) A "productive" fix of arima is attached (arma coefficients printed out and error catched so that it doesn't halt parent loops to search for

Is it possible to fit a lagged regression, "y[t]=b0+b1*x[t-1]+e", using the function "lag"? If so, how? If not, of what use is the function "lag"? I get the same answer from y~x as y~lag(x), whether using lm or arima. I found it using y~c(NA, x[-length(x)])). Consider the following: > set.seed(1) > x <- rep(c(rep(0, 4), 9), len=9) > y <-

hi: you should probably send below to R-Sig-Finance because there are some econometrics people over there who could also possibly give you a good answer and may not see this email ? Also, there's package called mar ( I think that's the name ) that may do what you want ? Finally, I don't know how to do it but I think there are ways of converting a multivariate arima into the

Hello all, I''m having fun again with the arima function. This time I read in: http://www.stat.pitt.edu/stoffer/tsa2/R_time_series_quick_fix.htm <<It has recently been suggested (by a reliable source) that using xreg in arima() does NOT fit an ARMAX model [insert slap head icon here]. This will be investigated as soon as time permits.>> (by R.H. Shumway & D.S. Stoffer)

As I am reading ?arima, only NA entries in the argument fixed= imports. The following seems to indicate otherwise: x <- arima.sim(model=list(ar=0.8), n=100) + (1:100)/50 > t <- 1:100 > mod1 <- lm(x ~ t) > > init1 <- c(0, coef(mod1)[2]) > fixed1 <- c(as.numeric(NA), 0) > > arima(x, order=c(1,0,0), xreg=t, include.mean=FALSE, init=init1, fixed=fixed1)

...e. This architecture has two single-ported register files. Each instruction can only read one operand from each register file, but can write to either. I tried implementing it naïvely in TableGen with two definitions per instruction, so I had: def AllRegs : RegisterClass< ... (add interleave (XRegs, YRegs))>; and in the InstrInfo.td: def Instr_xy: Instruction(outs AllRegs:$dst), (ins XRegs:$src1, YRegs:$src2), "...", [(set AllRegs:$dst, (OpNode XRegs:$src1, YRegs:$src2))] def Instr_yx: Instruction(outs AllRegs:$dst), (ins YRegs:$src1, XRegs:$src2), "...", [(set AllRe...

David Stoffer describes some challenges with R's output when fitting ARIMA models for different orders (see Issue 2 at http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm). R doesn't fit an intercept in the model if there is any differencing. David describes a workaround using the xreg parameter to force R to calculate an intercept. Assume I have a variable y and 3 explanatory variables a,

Dear all, I am fitting an arimax (arima with some extra explanatory variables) model to a time series. Say, I have a Y (dependent variable) and an X (explanatory). Y is 100 observations (time series) and X is 100 + 20 (20 to use for the forecast horizon). I can not make xreg work with the forecast function for an arima fit. The "predict" function seems to be working but the

Hello, Does anyone know how the parameter estimates are calculated for xreg variables when called as part of an arima() command, or know of any literature that provides this info? In particular, I was wondering if there is a quick way to compare different combinations of "xreg" variables in the arima() fit in the same way that you would in multiple regression (using AIC & R^2

Dear R users, I have an unbalanced panel with an average of I=100 individuals and a total of T=1370 time intervals, i.e. T>>I. So far, I have been using the plm package. I wish to estimate a FE model like: res<-plm(x~c+v, data=pdata_frame, effect="twoways", model="within", na.action=na.omit) ?where c varies over i and t, and v represents an exogenous impact on x

Hello. I've got a problem with arma/xreg. I would like to get a better model-fit by implenting some external explanatory variable, so I thought I can implement it by expand the arima-function with an xreg-argument: I have two stationary data vectors y and x of length 201: y <-

Hello everyone, Hope you all are doing great! I have been fitting arima models and performing forecasts pretty straightforwardly in R. However, I wanted to add a couple of regressors to the arima model to see if it could improve the accuracy of the forecasts but have had a hard time trying to do so. I used the following R function: arima(x, order = c(0, 0, 0), seasonal = list(order = c(0, 0,

I am having some issues with differencing using auto.arima when also specifying an xreg dataframe. The xreg dataframe contains dummy variables that specify time periods that had a promotion running. When I model diff(y) with order (1,0,1), the coefficients for these dummy variables are very different than when I model y with order=(1,1,1). I think when modeling diff(y) the coefficients

Hello I want to fit an AR model were two of the coefficients are fixed to zero (the second and third ar-coefficients). I used the "arima" function with the "fixed" argument but the ar3 coefficient is not set to zero: ============================================== > arima(Y, order=c(4,0,0), xreg=1:23, fixed=c(NA,0,0,NA,NA,NA)) Call: arima(x = Y, order = c(4, 0, 0), xreg =

Hello,guys: Recently, I am working on a seasonal ARIMA model. And I met some problem in the forecasting. Now I just want to know that How does R perform the predict procedure(the predict formula, the initial setting of errors,etc.)? I run the following commands and get the original code of the "predict" command, but I can't read it. Can anybody explain it to me? Thanks! saji from