similar to: Demean argument in ar function

Hello I am facing a curious problem.I have a time series data with which i want to fit auto-regressive model of order p, where p runs from 1:9.I am using a for loop which will fit an AR(p) model for each value of p using the *ar.ols* function. I am using the following code for ( p in 1:9){ a=ar.ols (x=data.ts, order.max=p, demean=T, intercept=T) } Specifying the *order.max* to be p, it gives me a

Hi there, we are beginners in R and we are trying to fit the following time series using ar(2): > x <- c(1.89, 2.46, 3.23, 3.95, 4.56, 5.07, 5.62, 6.16, 6.26, 6.56, 6.98, > 7.36, 7.53, 7.84, 8.09) The reason of choosing the present time series is that the we have previously calculated analitically the autoregressive coefficients using the direct inversion method as 1.1, 0.765, 0.1173.

Dears Sirs During my computational work I encountered unexpected behaviour when calling "ar" function. I want to select the order p of the autoregressive approximation by AIC criterion and sometimes an error occurs. Example: # time series

Full_Name: Steven McKinney Version: 2.6.0 OS: OS X Submission from: (NULL) (142.103.207.10) Function ar() in package "stats" is showing a quirky bug. Some calls to ar() run to completion, others throw an error. The bug is reproducible by several people on different machines, however, the ar() function itself ends up throwing the error sporadically. Several calls to ar() may be

Hi everyone I've got a problem regarding the arima() and the ar() function for autoregressive series. I would simply like to combine them. To better understand my question, I first show you how I'm using these two functions individually (see file in the attachement). 1) apply(TSX,2, function(x) ar(na.omit(x),method="mle")$order # this function finds the optimal

Hello, My name is Pedro and it is nice to meet you all. I am having trouble understanding a message that I receive when use function ar.ols from package stats, it says that "Warning message: In ar.ols(x = dtb[2:6966, ], demean = FALSE, intercept = TRUE, prewhite = TRUE) : model order: 2 singularities in the computation of the projection matrix results are only valid up to model order 1,

Suppose I have longitudinal data and want to use the econometric strategy of "de-meaning" a model matrix by time. For sake of illustration 'mat' is a model matrix for 3 individuals each with 3 observations where ``1'' denotes that individual i was in group j at time t or ``0'' otherwise. mat <- matrix(c(1,1,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,1,1,0),

?s 16:34 de 22/02/2024, Pedro Gavronski. escreveu: > Hello, > > My name is Pedro and it is nice to meet you all. I am having trouble > understanding a message that I receive when use function ar.ols from > package stats, it says that "Warning message: > In ar.ols(x = dtb[2:6966, ], demean = FALSE, intercept = TRUE, > prewhite = TRUE) : > model order: 2

There has been some recent discussion on this list about the value of using intervals with lme() to check for whether a model is ill-defined. My question is, what else can drive very large confidence intervals for the variance components (or cause the error message "Error in intervals.lme(Object) : Cannot get confidence intervals on var-cov components: Non-positive definite approximate

Hello, Thanks for the reply Rui and for pointing out that I forgot to attach my code. Please find attached in this email my code and data. Thanks in advance. Best regards, Pedro Gerhardt Gavronski. On Fri, Feb 23, 2024 at 5:50?AM Rui Barradas <ruipbarradas at sapo.pt> wrote: > > ?s 16:34 de 22/02/2024, Pedro Gavronski. escreveu: > > Hello, > > > > My name is Pedro

Dear List, I am using the output of a ML estimation on a random effects model with first-order autocorrelation to make a further conditional test. My model is much like this (which reproduces the method on the famous Grunfeld data, for the econometricians out there it is Table 5.2 in Baltagi): library(Ecdat) library(nlme) data(Grunfeld)

Hi All. I need to create a first-order autoregressive covariance matrix (AR(1)) for a longitudinal mixed-model simulation. I can do this using nested "for" loops but I'm trying to improve my R coding proficiency and am curious how it might be done in a more elegant manner. To be clear, if there are 5 time points then the AR(1) matrix is 5x5 where the diagonal is a constant

hello, can some body help me to get the residuals and the variance of coefficient after an autoregressive fit. These values are present in the ar function of the package ts. But I don't know how to makes it work. thank you. meriema -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

I was using ar(stats) to calculate autoregressive coefficient. It works on vector z, but it will not work on vector rz <-rank (z, ties.method="average").  What did I miss? Any info will be greatly appreciated.  TIA [[alternative HTML version deleted]]

Hello, Could anyone tell me what rules R uses to classify an autoregressive model i.e. how it decides the number of AR parameters to use when the AR(data) function is applied? I understood that model classification was more or less based on inspection of the acf and pacf functions. Thanks TT -- View this message in context:

Dear R users, I wish to manually demean a panel over time and entities. I tried to code the Wansbeek and Kapteyn (1989) transformation (from Baltagi's book Ch. 9). As a benchmark I use both the pmodel.response() and model.matrix() functions in package plm and the results from using dummy variables. As far as I understood the transformation (Ch.3), Q%*%y (with y being the dependent variable)

I had a couple of questions about the ar function that i was hoping someone could answer. I have the structure below testSeries<-structure(c(-3.88613620955214e-05, 0, -7.77272551011343e-05, 0, -0.000194344573539562, -0.000116624876218163, -3.88779814601281e-05, 0, 3.88779814601281e-05, -0.000155520995647807, -0.000116656621367561, -3.88885648225368e-05, -3.88900772017586e-05,

The data came through fine, the program was a miss. Can you paste the program into a ".txt" document like a notepad file and send that? You could also paste it into your email IF your email is configured to send text and NOT html. TIm -----Original Message----- From: R-help <r-help-bounces at r-project.org> On Behalf Of Pedro Gavronski. Sent: Friday, February 23, 2024 5:00 AM To:

Here is my problem: Autoregressive models are very interesting in forecasting consumptions (eg water, gas etc). Generally time series of this type have a long history with relatively simple patterns and can be useful to add external regressors for calendar events (holydays, vacations etc). arima() is a very powerful function but kalman filter is very slow (and I foun difficulties of estimation)

Dear R-users, I used lm() to fit a standard linear regression model to a given data set, which led to a coefficient of determination (R^2) of about 0.96. After checking the residuals I realized that they follow an autoregressive process (AR) of order 1 (and therefore contradicting the i.i.d. assumption of the regression model). I then used gls() [library nlme] to fit a linear