similar to: Regression and time series

This maybe a basic question, but I have spent several hours researching and I could not get an answer, so please bear with me. The problem is with time series in the package tseries. As the example below shows, the time series can get misaligned, so that bad results are obtained when doing regressions. I found a way to do this correctly, but I find it rather cumbersome. My question is: is there a

One thing that has given me trouble is the fact that the time series implementation in the class ts relies on the concept of a "start" to the time series. For example, if I have ts1 <- ts(c(1,2,3)) dts1 <- diff(ts1) then dts1 will be a vector c(1,1) but with the attribute start = 2. Similarly, when one takes lags the "start" is moved around and the underlying vector

This code gives an error: a <- ts(1:10, start=0, freq=10) b <- ts(1:10, start=1, freq=10) ts.intersect(a,b) This one works normally (and correctly): a <- ts(1:10, start=0) b <- ts(1:10, start=1) ts.intersect(a,b) Antonio, Fabio Di Narzo. P.S. How to switch off italian error messages to post on r-help? > version _ platform i386-pc-mingw32 arch i386 os

Hello, I apologize for this question that may has been asked a lot of times but I could not go through it. I create a multivariate time series containing NA values. I want to compute a linear regression and obtain a time serie for both residuals and fitted values. I have tried the trick ts.intersect, without success. Could you help me out of this? #### Example: y<-ts(1:10+rnorm(10))

Hello to all- 1. When I run a regression which implements the augmented Dickey-Fuller test, I am confused about the names given to the regressors in the output. I understand what "xGE" stands for in a standard "lm" test involving an independent variable GE for instance, but if I lags and or differences are included in the model, what do the following "output" stand

I tried to assign values to specific elements of a time series and got in trouble. The code below should be almost self-explanatory. I wanted to assign 0 to the first element of x, but instead I assigned zero to the second element of x, which is not what I wanted. Is there a function that will allow me to do this without going into index arithmetic (which would be prone to errors)? FS >

Hi, I am attempting to fit a ARMA/GARCH regression model without success. ### ARIMA-GARCH model with regressor ### ### Time series data: A multivariate data set. cov.ts.dq = cov.ts[1:4,"dq1"][!is.na(cov.ts[,"dq1"])] cov.ts.day = ts.intersect(dq = diff(q.ts), day = lag(q.ts, -1)) ### The following R scripts work: (summary(no.day.fitr <- garchFit(dq ~ arma(0,3) +

Dear readers, S-Plus have a rts function (Regular Time Series), which is used like so: fveks<-read.csv('http://louise.hoffman.googlepages.com/veks.csv',header=TRUE,sep=',') attach(fveks) acf(ts.intersect(rts(HC.f),rts(Ta.f),rts(GR.f),rts(W.f))) Warning the csv file is 750kB. Can the same be done in R? Lots of love =) Louise

Hello! I have such a problem... Estimated a model based on common data (you can find it in R library), and I wanted to plot the orginal values with the estimated one. Unfortunately I can only see the original values. Below is the code with data library: / library(forecast) data(AirPassengers) AP <- AirPassengers class(AP) start(AP) end(AP) frequency(AP) lgAP <- log(AP) t<-2:length(AP)

Hello: This question is partly about R and partly out of my ignorance about time series. I want to regress one time series on another, taking into account the autocorrelation (in an AR1 model) within each series. I am interested in how the standard error changes when the acf is taken into account. I've made both of my datasets into ts objects and used the basic lm function (with

I noticed that when one tries to extend a time series (ts) object by a single period using window() actually two observations (NAs) are added. See below for an example. Does anyone know the reason for this behavior and how to avoid it? Thanks. FS > x<-ts(c(1,2,3)) > x1<- window(x, start(x), end(x) + 1, extend = TRUE) > x1 Time Series: Start = 1 End = 5 Frequency = 1 [1] 1 2

This seems like a FAQ, but I can't figure it out. I have a mv ts object: R > tsp(pg) [1] 1982 2003 1 R > dim(pg) [1] 22 12 and a univariate ts: R > tsp(rw) [1] 1690 1996 1 Yet, when I try to intersect them: R > tsp(ts.intersect(rw, pg)) [1] 1982 2176 1 the process goes awry. How to I get rw and pg to be one ts that runs from 1982 to 1996 and has 13 univariate time

I am relatively new to R and object oriented programming. I have relied on SAS for most of my data analysis. I teach an introductory undergraduate forecasting course using the Diebold text and I am considering using R in addition to SAS and Eviews in the course. I work primarily with univariate or multivariate time series data. I am having a great deal of difficulty understanding and working with

Hi, I am using R interactively for estimation and even data manipulation. I want to use lag() function within lm() function in a way looks like: mymodel<- lm(y~x+I(lag(y,-1)-1, data=anydata) What I get is always perfect fit; (that is, coefficient=1) which is not true. If the model looks like mymodel<- lm(y~z+x+I(lag(x,-1)-1, data=anydata) summary returns only one coefficient for x and

Hi all, I'm trying to get the value of y when x=203 by using the intersect of three curves. The horizontal curve does not meet with the other two. How can I rectify the code below? Thanks Muhammad ts <- 1:10 dd <- 10:1 ts <- seq(200,209,1) dd <- c(NA,NA,NA,NA,1.87,1.83,1.86,NA,1.95,1.96) plot(ts,dd,ylim=c(1.5,2)) abline(lm(dd~ts),col="blue",lty=2)

Hello: I'm having trouble plotting an object of class "ts" that is in a data.frame. I can do it with(data.frame, plot(...)) but not with plot(..., data.frame); see the example below. This work around gets me past this problem. However, I thought the R Core team might want to know about this if they don't already. Thanks for all your work in making

I have a need to build a time series and there are a couple of aspects about the time series object that are confusing me. First it seems that ts.union is not doing what I would expect. For example: x0 <- rep(0,10) x1 <- rep(1,10) xt0 <- ts(x0, frequency=10) xt1 <- ts(x1, frequency=10) st2 <- ts.union(xt0, xt1) > xt2 Time Series: Start = c(1, 1) End = c(1, 10) Frequency = 10

On Fri, 1 Sep 2017, Andrea Altomani wrote: > I should have formulated my question in a more specific way. > > 1. I suspect this is a floating point precision issue. I am not very > knowledgeable about R internals, can someone else confirm it? Yes. If you represent a series with increment 1/12 it depends on how you do it. As a simple example consider the following two descriptions

Is this a bug? seasonal.dummies <- function(x, contr=NULL) { # takes a time series and returns a matrix of seasonal dummies for # x. This is almost cycle(x), we only have to make it into a factor # and add suitable level names. # return a matrix which includes a constant! # level names here assumes frequency is 12! cyc <- factor( cycle(x), labels=c("ene","feb",

I make quite a lot of use of irregular time-series, and had already spent a bit of time writing an 'its' class when the 'irts' class was released via the package 'tseries'. I have experimented with the 'irts' class, and have some practical issues with its use. In some applications of irregular time-series (in my case these are financial and econometric) there are