Displaying 20 results from an estimated 1000 matches similar to: "Restricted Least Squares"
2007 Mar 05
1
Heteroskedastic Time Series
Hi R-helpers,
I'm new to time series modelling, but my requirement seems to fall just
outside the capabilities of the arima function in R. I'd like to fit an
ARMA model where the variance of the disturbances is a function of some
exogenous variable. So something like:
Y_t = a_0 + a_1 * Y_(t-1) +...+ a_p * Y_(t-p) + b_1 * e_(t-1) +...+ b_q *
e_(t-q) + e_t,
where
e_t ~ N(0, sigma^2_t),
2007 May 08
2
statistics/correlation question NOT R question
This is not an R question but if anyone can help me, it's much
appreciated.
Suppose I have a series ( stationary ) y_t and a series x_t ( stationary
)and x_t has variance sigma^2_x and epsilon is normal
(0, sigma^2_epsilon )
and the two series have the relation
y_t = Beta*x_t + epsilon
My question is if there are particular values that sigma^2_x and
sigma^2_epsilon have to take in
2011 Dec 01
1
Estimation of AR(1) Model with Markov Switching
Dear R users,
I have been trying to obtain the MLE of the following model
state 0: y_t = 2 + 0.5 * y_{t-1} + e_t
state 1: y_t = 0.5 + 0.9 * y_{t-1} + e_t
where e_t ~ iidN(0,1)
transition probability between states is 0.2
I've generated some fake data and tried to estimate the parameters using the
constrOptim() function but I can't get sensible answers using it. I've tried
using
2010 Aug 23
1
Fitting a regression model with with ARMA error
Hi,
I want to fit a regression model with one independent variable. The error
part should be fitted an ARMA process.
For example,
y_t = a + b*x_t + e_t where e_t is modelled as an ARMA process.
Please let me know how do I do this in R. What code should I use?
TIA
Aditya
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2009 Jun 19
1
using garchFit() to fit ARMA+GARCH model with exogeneous variables
Hello -
Here's what I'm trying to do. I want to fit a time series y with
ARMA(1,1) + GARCH(1,1), there are also an exogeneous variable x which I
wish to include, so the whole equation looks like:
y_t - \phi y_{t-1} = \sigma_t \epsilon_t + \theta \sigma_{t-1}
\epsilon_{t-1} + c x_t where \epsilon_t are i.i.d. random
variables
\sigma_t^2 = omega + \alpha \sigma_{t-1}^2 + \beta
2013 Feb 05
1
R -HELP REQUEST
Good morning to you all,
Sorry for taking your time from your research and
teaching schedules.
If you have a non-stationary univariate time Series
data that has the transformation:
Say; l.dat<-log (series)
d.ldat<-diff (l.dat, differences=1)
and you fit say arima model.
predit.arima<-predict (fit.series, n.ahead=10,
xregnew= (n+1) :( n+10))
How could I re-transform
2008 Sep 10
2
arima and xreg
Dear R-help-archive..
I am trying to figure out how to make arima prediction when I have a
process involving multivariate time series input, and one output time
series (output is to be predicted) .. (thus strictly speaking its an
ARMAX process). I know that the arima function of R was not designed
to handle multivariate analysis (there is dse but it doesnt handle
arma multivariate analysis, only
2012 Jul 28
4
quantreg Wald-Test
Dear all,
I know that my question is somewhat special but I tried several times to
solve the problems on my own but I am unfortunately not able to compute the
following test statistic using the quantreg package. Well, here we go, I
appreciate every little comment or help as I really do not know how to tell
R what I want it to do^^
My situation is as follows: I have a data set containing a
2002 Apr 19
4
Durbin-Watson test in packages "car" and "lmtest"
Hi,
P-values in Durbin-Watson test obtained through the use of functions available in packages "lmtest" and "car" are different. The difference is quite significant. function "dwtest" in "lmtest" is much faster than "burbinwatson" in "car". Actually, you can take a nap while the latter trying to calculated Durbin-Watson test. My question
2009 Nov 02
1
AR Simulation with non-normal innovations - Correct
Dear Users,
I would like to simulate an AR(1) (y_t=ct1+y_t-1+e_t) model in R where the innovations are supposed to follow a t-GARCH(1,1) proccess.
By t-GARCH I want to mean that:
e_t=n_t*sqrt(h_t) and
h_t=ct2+a*(e_t)^2+b*h_t-1.
where n_t is a random variable with t-Student distribution.
If someone could give some guidelines, I can going developing the model.
I did it in matlab, but the loops
2004 Jan 14
3
How can I test if time series residuals' are uncorrelated ?
Ok I made Jarque-Bera test to the residuals (merv.reg$residual)
library(tseries)
jarque.bera.test(merv.reg$residual)
X-squared = 1772.369, df = 2, p-value = < 2.2e-16
And I reject the null hypotesis (H0: merv.reg$residual are normally
distributed)
So I know that:
1 - merv.reg$residual aren't independently distributed (Box-Ljung test)
2 - merv.reg$residual aren't indentically
2000 Apr 04
0
stochastic process transition probabilities estimation
Hi all,
I'm new with R (and S), and relatively new to statistics (I'm a
computer scientist), so I ask sorry in advance if my question is silly.
My problem is this: I have a (sample of a) discrete time stochastic
process {X_t} and I want to estimate
Pr{ X_t | X_{t-l_1}, X_{t-l_2}, ..., X_{t-l_k} }
where l_1, l_2, ..., l_k are some fixed time lags. It will be enough for
me to compute
2007 May 21
1
Sample correlation coefficient question NOT R question
This is a statistics question not an R question. When calculating the
sample correlation coefficient cor(x_t,y_t) between say
two variables, x_t and y_t t=1,.....n ( one can assume that the
variables are in time but I don't think this really matters
for the question ), does someone know where I can find any piece of
literature that says that each (x_j,y_j) pair has
To be independent from the
2013 Jan 03
2
simulation
Dear R users,
suppose we have a random walk such as:
v_t+1 = v_t + e_t+1
where e_t is a normal IID noise pocess with mean = m and standard deviation = sd and v_t is the fundamental value of a stock.
Now suppose I want a trading strategy to be:
x_t+1 = c(v_t – p_t)
where c is a costant.
I know, from the paper where this equations come from (Farmer and Joshi, The price dynamics of common
2005 Mar 05
4
How to use "lag"?
Is it possible to fit a lagged regression, "y[t]=b0+b1*x[t-1]+e",
using the function "lag"? If so, how? If not, of what use is the
function "lag"? I get the same answer from y~x as y~lag(x), whether
using lm or arima. I found it using y~c(NA, x[-length(x)])). Consider
the following:
> set.seed(1)
> x <- rep(c(rep(0, 4), 9), len=9)
> y <-
2011 Nov 22
1
arima.sim: innov querry
Apologies for thickness - I'm sure that this operates as documented and with good reason. However...
My understanding of arima.sim() is obviously imperfect. In the example below I assume that x1 and x2 are similar white noise processes with a mean of 5 and a standard deviation of 1. I thought x3 should be an AR1 process but still have a mean of 5 and a sd of 1. Why does x3 have a mean of ~7?
2005 Jun 01
2
Fitting ARMA model with known inputs.
Hello!
Is it possible to use R time series to identificate a process which is
subjected to known input? I.e. I have 2 sequences - one is measurements
of black box's state and the second is the "force" by which this black
box is driven (which is known too) and I want to fit thist two series
with AR-process. The "ar" procedure from stats package expects that the
force is
2006 Apr 29
1
SSPIR problem
I am having a problem with the package SSPIR. The code below
illustrates it. I keep getting the message: "Error in y - f :
non-conformable arrays."
I tried to tweak the code below in many different ways, for example,
substituting rbind for cbind, and sometimes I get a different error
message, but I could not find a variation of this code that would
work.
Any help will be greatly
2012 Oct 11
2
ccf(x,y) vs. cor() of x and lagged values of y
Hi
I'm computing the correlation between two time-series x_t and y_t-1
(time-series lagged using the lag(y,-1) function) using the cor() function
and the returned value is different from the value of ccf() function at the
same lag. Any ideas why this is so?
Thanks in advance for any hints.
Mihnea
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2010 Mar 11
1
VAR with contemporaneous effects
Hi,
I would like to estimate a VAR of the form:
Ay_t = By_t-1 + Cy_t-2 + ... + Dx_t + e_t
Where A is a non-diagonal matrix of coefficients, B and C are matricies of
coefficients and D is a matrix of coefficients for the exogenous variables.
I don't think the package {vars} can do this because I want to include
contemporaneous cross-variable impacts.
So I want y1_t to affect y2_t and I