Displaying 17 results from an estimated 17 matches for "nonstationary".
2013 Feb 22
2
Model selection in nonstationary VAR
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 understand that the usual pro...
2005 May 13
1
clustering
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 confus...
2006 Feb 06
1
marginal distribution wrt time of time series ?
...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 for weak dependent, stationary
and ergodic time series such a 'marginal distribution
w.r. to time' converges to marginal distribution
of random variable x_t , defined on basis of joint
distribution for (x_1,&...
2007 Oct 30
2
markov regime switching models
...while the package I need should allow to set a conditional distribution
(given the state) which can be still modelled (for example with
mean-reversion or jump diffusion...).
I think the theory under this estimation technique is in "James D. Hamilton,
A New Approach to the Economic Analysis of Nonstationary Time Series and the
Business Cycle (1989)"
Thanks very much for any help!
Valentina Bonetti
Master student at Bocconi University, Milan
[[alternative HTML version deleted]]
2009 Jan 21
1
Multifractal detrended fluctuation analysis
...nalysis? 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 Nonstationary Time Series," Physica A 316, 87-114 (2002).
Thanks for any help you can provide.
Sincerely,
J. Dixon
--
James A. Dixon
Department of Psychology
406 Babbidge Road, Unit 1020
University of Connecticut
Storrs, CT 06269-1020
Phone: (860)486-6880
Fax: (860)486-2760
email: james.dixon@uconn.edu...
2010 Jan 31
0
Package ismev, gpd.fit, and interpretation for statistics of extreme values
...ations. The date covers 1991 to 2008. I
created a time variable indexed by day, ranging from 1 (i.e.,
July/1/1991) to 6,708 (Nov/10/2008). In total, there 6,708 days. I used
function "gpd.fit" to estimate two threshold models, one without time
and the other with time (i.e., stationary vs. nonstationary). The key
outputs are attached as follows.
My first question is how to interpret the coefficient for time trend (0.
00473462). Does it mean the fire size increase by 0.00473462 acre per
day? My second question is related to calculating return levels. There
are multiple fires on individual calen...
2011 Nov 06
1
VAR and VECM in multivariate time series
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 difference of it to make it stationary.
library(tsDyn)
VARselect(y,lag.max=20,type="const",season = NULL, exogen = NULL)
y1=VAR(y, p = 16, type...
2003 Jul 08
1
Questions about corARMA
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
2005 Oct 04
2
Need help on ARIMA (time series analysis)
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
2003 Aug 07
0
correlation matrix
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
_________________________________________________________________
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2007 Oct 22
1
Newbie help: Data in an arma fit
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
2008 Sep 10
0
MA coefficients
...665.43
> arima(residuals, order=c(2,1,1))
Call:
arima(residuals, order = c(2,1, 1))
Coefficients:
ar1 ar2 ma1
-0.4196 -0.3328 -1.0000
s.e. 0.0861 0.0857 0.0215
sigma^2 estimated as 0.0002529: log likelihood = 320.83, aic = -633.66
(a) Did this indicate a nonstationary/noninvertible process?
(b) Did the algorithm converge? Would you trust the fit??
(c) What would you do next?
Best,
Ricardo
[[alternative HTML version deleted]]
2012 Sep 24
0
Estimated covariance matrix with tgp package
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 indepe...
2013 Feb 28
0
R and S+ Courses: Brisbane, Melbourne & Sydney
...FinMetrics (2 Day)
Course is designed 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 &a...
2007 Nov 26
3
Time Series Issues, Stationarity ..
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 =
2010 Oct 29
3
Dickey Fuller Test
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
2010 Jul 18
6
CRAN (and crantastic) updates this week
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