Displaying 20 results from an estimated 1000 matches similar to: "Sample correlation coefficient question NOT R question"
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
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 Feb 01
3
Help with efficient double sum of max (X_i, Y_i) (X & Y vectors)
Greetings.
For R gurus this may be a no brainer, but I could not find pointers to
efficient computation of this beast in past help files.
Background - I wish to implement a Cramer-von Mises type test statistic
which involves double sums of max(X_i,Y_j) where X and Y are vectors of
differing length.
I am currently using ifelse pointwise in a vector, but have a nagging
suspicion that there is a
2002 Apr 09
2
Restricted Least Squares
Hi,
I need help regarding estimating a linear model where restrictions are imposed on the coefficients. An example is as follows:
Y_{t+2}=a1Y_{t+1} + a2 Y_t + b x_t + e_t
restriction
a1+ a2 =1
Is there a function or a package that can estimate the coefficient of a model like this? I want to estimate the coefficients rather than test them.
Thank you for your help
Ahmad Abu Hammour
--------------
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),
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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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
2010 Oct 06
1
dlm package: how to specify state space model?
Dear r-users!
I have another question regarding the dlm package and I would be very
happy if someone could give me a hint!
I am using the dlm package to get estimates for an endogenous rate of
capacity utilization over time. The general form of a state space model
is
(1) b_t = G * b_t-1 + w_t w_t ~ N(0,W)
(2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V)
(Hamilton 1984: 372)
The
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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2013 May 02
2
ARMA with other regressor variables
Hi,
I want to fit the following model to my data:
Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t
i.e. it is an ARMA(2,2) with some additional regressors X and M.
[Z_t's are the white noise variables]
How do I find the estimates of the coefficients in R?
And also I would like to know what technique R employs to find the
estimates?
Any help is appreciated.
Thanks,
2000 Sep 22
0
what do you do for 2SLS or 3SLS
For 2 or 3 stage least squares, what do you R folks do?
Follow-up question. My student wants to estimate this. 2 variables are
governed by a system of difference equations. His theory is like so.
Y_t and X_t are
state variables, we want estimates for a, g, b, and h.
X_(t+1) = 1 + a X_t + (a/K)* (X_t)^2 - g Y_t X_t
Y_(t+1) = b Y_t + h* X_t * Y_t
K is perhaps something to estimate, but it
2002 Aug 13
1
Ex ante forecasting from structural equation models (SEM package)
Dear Helplist,
I want to produce forecasts from a structural equation model. With the SEM
package the model setup and its estimation is possible. However, I have not
figured out how to obtain ex ante forecasts, i.e. applying the Gauss-Seidel
algorithm to the estimated structural equations for provided values of the
exogenous variables (i.e.: y_t = -inv(A)*B*x_t).
Does anyone know if the there is
2004 Aug 06
1
Elementary questions about data structures
Folks,
S_t = (x_t, y_t) is the state of a system at time t. There is an
iterative function which converts S_t into S_{t+1}. I can easily write
this in R like this:
iterate <- function(S) {
list(S$x+1, S$y+1)
}
So this function eats S_t and makes S_{t+1} and I can say
S2 <- iterate(S1)
My question: suppose I want to iterate from 1..10, what is the data
structure
2013 May 02
1
warnings in ARMA with other regressor variables
Hi all,
I want to fit the following model to my data:
Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t
i.e. it is an ARMA(2,2) with some additional regressors X and M.
[Z_t's are the white noise variables]
So, I run the following code:
for (i in 1:rep) { index=sample(4,15,replace=T)
final<-do.call(rbind,lapply(index,function(i)
2006 Dec 20
2
Kalman Filter in Control situation.
I am looking for a Kalman filter that can handle a control input. I thought
that l.SS was suitable however, I can't get it to work, and wonder if I am
not using the right function. What I want is a Kalman filter that accepts
exogenous inputs where the input is found using the algebraic Ricatti
equation solution to a penalty function. If K is the gain matrix then the
exogenous input
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 <-
2004 Apr 07
1
Time Varying Coefficients
I'd like to estimate time varying coefficients in a linear regression using
a Kalman filter.
Even if the Kalman Filter seems to be available in some packages I can't
figure out how to use it to estimate the coefficients.
Is there anyway to do that in R?
Any help appreciated
Thanks
2024 Jan 23
0
Quantiles of sums of independent discrete random variables
Greetings,
I have the following?
Problem:
Given k (=10) discrete independent random variables X_i with n_i (= 5 to 20) values each,compute quantiles of the distribution of the sum X = X_1+...+X_k.
Here X has n=n_1 x n_2 ... n_k distinct values which is too large to list them all together with
their probabilities.
I tried several approaches:
(A) Convolution:
each X_j is approximated with
2009 Apr 02
1
matrix vectorization or something else??
Hello
This may have been answered elsewhere, and I have looked on the web, but nothing helps. I am trying to do the following:
X<-matrix(c(1:15),nrow=3,byrow=T)
Y<-matrix(c(2,4,6,8,10),ncol=1)
I need to sum the product of each row of X by the remaining j rows multiplied by j y values (i.e sum( t(x_i) x_j y_j) )
Hope this makes sense.
Thanks in advance.
Ps: how do I reference all
2010 Sep 28
0
Time invariant coefficients in a time varying coefficients model using dlm package
Dear R-users,
I am trying to estimate a state space model of the form
(1) b_t = G * b_t-1 + w_t w_t ~ N(0,W)
(2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V)
(Hamilton 1984: 372)
In particular my estimation in state space form looks like
(3) a3_t = 1 * a3_t-1 + w_t w_t ~ N(0,W)
(4) g_t = (a1, a2) * (1, P_t)' + u_t * a3_t + v_t v_t ~ N(0,V)
where g_t is the