similar to: correlation matrix

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Hi all, Can I run a cointegrating regression, for example delta Xt=a1(Yt-1-cXt-1)+E1t and delta Yt=-b1(Yt-1-cXt-1)+E2t with R were Xt and Yt are non stationary time series at t a,b,c are parameters and E1t and E2t are error terms at t. Yt-Xt is stationary Any suggestions are welcome. Best regards, /fb -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing

Hello, here is another question, how do I generate random walk models in R? Basically, I need an AR(1) model with the phi^1 value equal to 1: Yt = c + Yt-1 + E where E is random white noise. I tried using the arima.sim command: arima.sim(list(ar=c(1)), n = 1000, rand.gen = rnorm) but got this error since the model I am generating is not stationary: Error in arima.sim(list(ar = c(1)), n =

Dear All, I have a question about package "ismev", its function "gpd.fit", and interpretation of the results. I used the package ismev to do an extreme value analysis on a fire dataset. Two variables are used in the analysis. The focal variable is acreage burned per fire, ranging from 1 to 5000 acres per fire. In total, there are 69,980 observations. The date covers

Folks, Is there any implementation available in R for the simultaneous selection of lag order and rank of a nonstationary VAR as described in Chao & Phillips (1999): Model selection in partially nonstationary vector autoregressive processes with reduced rank structure, J. Econ. (91). Or any other systematic procedure for the consistent selection of lag order and cointegration rank? I

Dear all, In many papers regarding time series analysis of acquired data, the authors analyze 'marginal distribution' (i.e. marginal with respect to time) of their data by for example checking 'cdf heavy tail' hypothesis. For i.i.d data this is ok, but what if samples are correlated, nonstationary etc.? Are there limit theorems which for example allow us to claim that

Hey, I have a few doubts with regard to the usage of the auto.arima function from the forecast package in R. *Background:* I have a set of about 50 time-series for which I would like to estimate the best autroregressive model. (I want to estimate the coefficients and order of p). Each of the series is non-stationary and are also have a non-normal distribution. The data is non-seasonal. My

Hello to everyone! I am working on my final year project about multivariate time series. There are three variables in the multivariate time series model. I have a few questions: 1. I used acf and pacf plot and find my variables are nonstationary. But in adf.test() and pp.test(), the data are stationary. why? 2.I use VAR to get a model. y is the matrix of data set and I have made a once

Hi, Assume that we may model the Nottingham temperature data (nottem) or Sunspot data (sunspot) set by a nonparametric autoregressive model of the form Yt = m(Yt-1) + et. Using the kernel estimation method, produce the resulting plots. We may use the fucntion tsmooth(x,y,"notmal",bandwidth=0.01). How can i define x and y using data nottem and sunspot?

{ I think this message got rejected at the 1st attempt - trying again} R 2.15.1 , windows XP I have a very non-stationary bivariate time-series - say {xt,yt} t=1 ... lots. I want to do a bivariate density contour-plot of the whole series and then step through the series 1 second at a time plotting that second's {x,y} subset on top of the contour plot and losing the previous

We are trying to build a human respiration model. Preliminary analysis of some breathing signals has shown that humans breathe through switching among a finite number of patterns. Hidden Markov seems to be the right approach. Since most of our code is written in R scripting language, finding an R package implementing an HMM that we can use for our prototype would be very helpful. I have been

When finally got some time to do some coding, I started and stopped right after. The stationary test is a good starting point because it demonstrates how we should be able to move the very basic R matrices. I have a real- world small N data set with rows: id(n=1)---t1---variable1 ... id=(N=20)---T=21---variable1 Thus, a good test case. For first id I was considering something like this: lag

Hi all, Does anyone have an example of an autoregressive (AR) time-series model specified as a state space model in R? That is, I want to go beyond the locally linear (constant) model, and fit the following Gaussian AR state process model: Xt = a + (1+b)*Xt-1 + epsilon ,where the model for the observation process is Yt = Xt + tau I have information of the tau's (observation variance)

Hello, I am very new to R and Time Series. I need some help including R codes about the following issues. I' ll really appreciate any number of answers... # I have a time series data composed of 24 values: myinput = c(n1,n2...,n24); # In order to make a forecasting a, I use the following codes result1 = arima(ts(myinput),order = c(p,d,q),seasonal = list(order=c(P,D,Q))) result2 =

Short question: Is it possible to use statistical tests, like the Augmented Dickey-Fuller test, in functions with for-loops? If not, are there any alternative ways to scale measures? Detailed explanation: I am working with time-series, and I want to flag curves that are not stationary and which display pulses, trends, or level shifts. >df DATE ID VALUE2012-03-06 1

Dear all, I am working on modifying the R working matrix to commodate some other correlations that not included in the package. I am having problem to locate where the R matrix are defined for regular matrices, i.e. independence, exchangeable, AR and unstructure. it might have something within .C("Cgee",but don't understand it well enough to know. Can you anyone help? /*gee source

Hi you all, I have couple of questions regarding how to use the vars package (the vector autoregression model) to find co-relation/ impact between multiple time series. I am not majoring in economic, I just want to use vars to check how those time series I had impacting each other. I also hope this post can give those non-economic majors a step by step guide on how to start using the vars

This is not exactly an R question but the R code below may make my question more understandable. If one plots sin(x) where x runs from -pi to pi , then the curve hovers around zero obviously. so , in a"stationary in the mean" sense, the series is stationary. But, clearly if one plots the acf, the autocorrelations at lower lags are quite high and, in the "box jenkins"

Hi, I'm a new member here in the list. I am a graduate from University of Georgia. Recently in doing analysis using lme on a dataset, I found several questions: 1. How to express the equation when the correlation structure is very complicated. For exmaple, if the fixed is y(t)=0.03x1(t)+1.5x2(t)(I omitted "hat" and others). And the model with corARMA(p=2,q=3) is proper. What will be

Dear Users, please help with the following DF test: ===== library(tseries) library(timeSeries) Y=c(3519,3803,4332,4251,4661,4811,4448,4451,4343,4067,4001,3934,3652,3768 ,4082,4101,4628,4898,4476,4728,4458,4004,4095,4056,3641,3966,4417,4367 ,4821,5190,4638,4904,4528,4383,4339,4327,3856,4072,4563,4561,4984,5316 ,4843,5383,4889,4681,4466,4463,4217,4322,4779,4988,5383,5591,5322,5404