search for: nonstationari

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

Hi Every body, In order to deal with nonstationary problem in time series, may be firstly clustering algorithms are used to partition time series .Then another algorithm is used to predict future value based on segmented data in the second phase. Using clustering algorithms , the "time structure and arrangement" of time series is confused. We have some partitions including data

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

Hi, I am looking for a package to estimate regime switching models (states following a markov chain). I found packages for Hidden Markov Models but I am looking for something a little different: In the HMM the conditional distribution of the observations (give the state) is a known distribution (normal or others), while the package I need should allow to set a conditional distribution (given the

Dear R-users, Has anyone written a function for multifractal detrended fluctuation analysis? The "fractal" package does mono-fractal DFA, but not multifractal as far as I can tell. The MF-DFA approach is presented in: J. W. Kantelhardt, S. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, and H. E. Stanley, "Multifractal Detrended Fluctuation Analysis of

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

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, 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

Hi, I am so novice in using R. I have some problems in my R script below which fits time series data and predict it one-step ahead. Here is a brief explanation on what I try to achieve Th16k is time series data (500 data points). The size of window for fitting and predicting is 50 (data points). As you can easily discover from my code, (fixed) window is moving/sliding to get next one-step

Dear all, I have T Y variables, Y1,...,YT, which are T repeated observations on a variable over the T waves of a survey. I'd like to estimate a correlation matrix cor(Y1,...,YT) assuming specific structures, as for example exchangeable, stationary, autoregressive and nonstationary. Is there any command/package in R that I could use? Thanks in advance. Marcel

I'd like to fit an ARMA(1,1) model to some data (Federal Reserve Bank interest rates) that looks like: ... 30JUN2006, 5.05 03JUL2006, 5.25 04JUL2006, N <---- here! 05JUL2006, 5.25 ... One problem is that holidays have that "N" for their data. As a test, I tried fitting ARMA(1,1) with and without the holidays deleted. In other words, I fit the above data

Hi everyone, I am performing the time series regression analysis on a series of data sets. A few data sets followed an ARMA(1,1) process. However, they all had a same value of moving average MA coefficients = -1, constantly, from output of function “arima" . Example: > arima(residuals, order=c(1,0,1)) Call: arima(residuals, order = c(1, 0, 1)) Coefficients:          ar1      ma1  intercept

Hello everyone, at the moment I'm using the tgp package for modelling a nonstationary data set on a two dimensional area D and I'm interested in the prediction and the estimated covariance matrix. For this purpose I'm using the function btgp. As far as I understand, btgp uses a MCMC algorithm to split up D along lines parallel to the coordinate axes and estimates independent

...to show how S+ and S+FinMetrics can be used in the analysis of financial data. Topics and methodologies covered include: S-Language, S+ and S+FinMetrics; Specification, Manipulation and Visualization of Financial Data; Univariate Stationary Models; Univariate Nonstationary Models; Stationarity and Nonstationarity Unit Root Tests; Long Memory Models; Vector Autoregressive Models; Cointegration Models; Univariate GARCH Models; Multivariate GARCH Models and Threshold/Switching Models. More Info<http://bit.ly/Xf9fKV> Date: 13 & 14 June, 2013 - Sydney (Mon & Tue) Cheers Kris Kris Angelovski...

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 =

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

CRAN (and crantastic) updates this week New packages ------------ * allan (1.0) Alan Lee http://crantastic.org/packages/allan Automates Large Linear Analysis Model Fitting * andrews (1.0) Jaroslav Myslivec http://crantastic.org/packages/andrews Andrews curves for visualization of multidimensional data * anesrake (0.3) Josh Pasek http://crantastic.org/packages/anesrake This