similar to: Regression with time-dependent coefficients

Hi, Following convention below: y(t) = Ax(t)+Bu(t)+eps(t) # observation eq x(t) = Cx(t-1)+Du(t)+eta(t) # state eq I modified the following routine (which I copied from: http://www.stat.pitt.edu/stoffer/tsa2/Rcode/Kall.R) to accommodate u(t), an exogenous input to the system. for (i in 2:N){ xp[[i]]=C%*%xf[[i-1]] Pp[[i]]=C%*%Pf[[i-1]]%*%t(C)+Q siginv=A[[i]]%*%Pp[[i]]%*%t(A[[i]])+R

Dear R, I have find a website where they report problem with ARIMA models in R. I run the examples there and they give result as shown on the website. Does this mean that nothing has corrected in R? Maybe you not have seen the page, but the author said he contacted you. Here is the URL: http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm I like to know your opinion. Mvh. Marie [[alternative

Dear R, I have find a website where they report problem with ARIMA models in R. I run the examples there and they give result as shown on the website. Does this mean that nothing has corrected in R? Maybe you not have seen the page, but the author said he contacted you. Here is the URL: http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm I like to know your opinion. Mvh. Marie [[alternative

Hi, I have been using this website ( http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm ) to help me to fit ARIMA models to my data. At the moment I have two possible methods to use. Method 1 If I use arima(ts.data, order=c(1,2,0), xreg=1:length(ts.data)) then the wrong value for the intercept/mean is given (checked on SPSS and Minitab) and

Hi All, I have a very simple question about a time series object: how to access values for a particular year and quarter (say)? Suppose, following http://www.stat.pitt.edu/stoffer/tsa2/R_time_series_quick_fix.htm I have read in data as a time series; here is how it looks. * Qtr1 Qtr2 Qtr3 Qtr4 1960 0.71 0.63 0.85 0.44 1961 0.61 0.69 0.92 0.55 . . . . .

I am trying to understand how to fit an ARMAX model with the arima function from the stats package. I tried the simple data below, where the time series (vector x) is generated by filtering a step function (vector u, the exogenous signal) through a lowpass filter with AR coefficient equal to 0.8. The input gain is 0.3 and there is a 0.01 normal white noise added to the output: x <- u

Hi My name is Vladimir Samaj. I am a student of Univerzity of Zilina. I am trying to implement Kalman Filter into my school work. I have some problems with understanding of R version of Kalman Filter in package stats( functions KalmanLike, KalmanRun, KalmanSmooth,KalmanForecast). 1) Can you tell me how are you seting the initial values of state vector in Kalman Filter? Are you using some method?

Dear all, When I run an arima(1,1,1) on an I(1) variable, y, I get different estimates to when I first difference the variable myself, e.g y2<-diff(y), and then run arima(1,0,1) on y2. Shouldn't these two approaches give the same output? Any help will be much appreciated. george

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)

Hi, My query is related to ARIMA function in stats package. While looking for the time series literature I found following link which highlights discrepancy in "arima" function while dealing with differenced time series. Is there a substitute function similar to "sarima" mentioned in the following website implemened in R? Any pointers would be of great help.

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

Hi, I am trying to learn time series, and I am attending a colleague's course on Econometrics. However, he uses e-views, and I use R. I am trying to reproduce his examples in R, but I am having problems specifying a AR(1) model. Would anyone help me with my code? Thanks in advance! Reproducible code follows: download.file("https://sites.google.com/a/proxima.adm.br/main/ex_32.csv

Hi, I have a suggestion for the fonction arima and arima0. I think you should not call the constant an intercept because it creates confusion. It is not really an intercept but a mean. For an AR(1) the intercept mu should be defined as: X(t)=mu + phi X(t-1) + e(t) What you call intercept mu is rather defined as (X(t)-mu) = phi (X(t-1)-mu)) + e(t) which is not a common way to define an

Hi all, I would like to know whether there is any function in R were i can find the cross-correlation of two or more multivariate (time series) data. I tried the function ccf() but it seems like to have two univariate datasets. Please let me know. sincerely, sandeep -- Sandeep Joseph PhD Post Doctoral Associate Center for Tropical & Emerging Global Diseases Paul D. Coverdell Center,

Hi, Does anyone know if R has a function that is similar to lag.plot but instead of auto-correlation, it plots cross-correlation with lags? Thanks, -- Tom [[alternative HTML version deleted]]

Hello,Could somebody tell me what is the difference between  theese 3 calls of functionsarma(x,order=c(1,0)), arima(x,order=c(1,0,0)) ar(x,order=1)?I expected same residuals of theese three models,but unexpectably for the first two R requiredinitial value of something (what?)...Thanks in advance!  [[alternative HTML version deleted]]

Hello everybody! I have a problem when I try to perform a forecast of an ARIMA model produced by an auto.arima function. Here is what I'm doing: c<-auto.arima(fil[[1]],start.p=0,start.q=0,start.P=0,start.Q=0,stepwise=TRUE,stationary=FALSE,trace=TRUE) # fil[[1]] is time series of monthly data ARIMA(0,0,0)(0,1,0)[12] with drift : 1725.272 ARIMA(0,0,0)(0,1,0)[12] with drift

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 apologize in advance if I picked the wrong list to post this to. I have made an effort to find the answers to these questions on CRAN, but if they are there, I couldn't find them, and I was going to email the developer of StructTS directly but could not find who that is. I have 2 interrelated questions about StructTS 1. Where can I obtain the source code for StructTS if I wanted to

Hi, I want to give a summary or anova for "arima" model in R, as "summary", and "anova" for "lm". As including various intervention factors in arima(xreg = ) part, I want to assess the significancy of thse factors. I can do it using interrupted analysis of time series by linear regression, but want to see whether arima model works for the data first.