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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 order for corr(x_t,y_t) t...

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 seems as though somebody has a formula we can use to calculate it from data and people just want to plug...

Hi, I've been searching CRAN and the web for a recursive multivariate filter with time-varying coefficients. What I mean is the following: I have a series of square matrices A_t an initial value vector y_0 and I need to compute y_t =A_t%*%y_t-1 As these y_t may diverge quickly and/or lead to underflow problems, the y_t need to be scaled by eg y_t =y_t/sum(y_t-1) Is anyone aware whether this has been implemented somewhere? Best, Ingmar

...of output data) number of rows.. So in fact I can let the different columns of xreg to actually be the different input time series I need! Is anyone familiar in how arima with xreg as given estimate models? .. how is the model assumed? supposing I write : arima(y, xreg=U, order=c(3,0,2)) how is y_t calculated? (supposing U has 2 columns, with U[1] being first column and U[2] second column) is it y_t = theta_(t-1)y_t-1 + .... + theta_t-3 y_t-3 + intercept + U[1]_t + psi[1]_t-1 U[1]_t-1 + psi[1]_t-2 U[1]_t-2 + ....+ psi[2]U[2]_t-2 + e_t + phi_t-1 e_t-1 + phi_t-2 e_t-2 ?? e_t .. etc. are th...

I have a question and it's only relation to R is that I probably need R after I understand what to do. Both models are delta y_t = Beta + epslion and suppose I have a null hypothesis and alternative hypothesis H_0 : delta y_t = zero + epsilon epsilon is normal ( 0, sigmazero^2 ) H_1 delta y_t = beta + epsilon epsilon is normal ( sigmabeta^2 ) ----------------------------------...

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 other (x_i,y_...

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

...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 (dependent) vector Y and the regressor X. My aim is to check whether the two variables do not granger-cause each other in quantiles. I started to compute via quantreg for a single tau:= q: rq(Y_t~Y_(t-1)+Y_(t-2)+...+X_(t-1)+X_(t-2)+...,tau=q) This gives me the quantile regression coefficients. Now I want to check whether all the coefficients of X are equal to zero (for this specific tau). Can I do this by applying rq.anova ? I have already asked a similiar question but I am not sure if ano...

I am a beginner in using R and I need help in the interpretation of AR result by R. I used 12 observations for my AR(2) model and it turned out the intercept showed 5.23 while first and second AR coefficients showed 0.40 and 0.46. It is because my raw data are in million so it seems the intercept is too small and it doesn't make sense. Did i make any mistake in my code? My code is as follows:

...Shurly I can try to implement an external function and use it with R, but this probably wouldn't be as simple as to write the code in R itself. An idea should be to use R in a "multivariate" fashion: if I can get factors of a multivariate random variable, I can build a random vector Y_t using X_t, and its lags: Y_t = [ X_t, X_{t-l_1}, X_{t-l_2}, ..., X_{t-l_k} ], and taking the factors of Y_t and of its margin without the first component I think I'm done. Unfortunately, factor, sort, unique all work only with univariate data. Any other suggestion will be very appreciated......

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 <-

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 tr...

....ind,i.ind))==1,]) D<-data.matrix(cbind(D1,D2)) Q<-I-D%*%ginv(crossprod(D))%*%t(D)#fQ(D1)-fQ(D1)%*%D2%*%ginv(t(D2)%*%fQ(D1)%*%D2)%*%t(D2)%*%fQ(D1) Q } ############################## library(plm) library(lmtest) data(Grunfeld) y_i<-Grunfeld$inv-ave(Grunfeld$inv,index=Grunfeld$firm) y_t<-Grunfeld$inv-ave(Grunfeld$inv,index=Grunfeld$year) y_it<-(Grunfeld$inv-ave(Grunfeld$inv,index=Grunfeld$firm)-ave(Grunfeld$inv,index=Grunfeld$year)+rep(mean(Grunfeld$inv),length(Grunfeld$inv))) x1_it<-(Grunfeld$value-ave(Grunfeld$value,index=Grunfeld$firm)-ave(Grunfeld$value,index=Grunfeld...

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 develo...

...et the > different columns of xreg to actually be the different input time > series I need! > > Is anyone familiar in how arima with xreg as given estimate models? .. > how is the model assumed? > > supposing I write : > > arima(y, xreg=U, order=c(3,0,2)) > > how is y_t calculated? (supposing U has 2 columns, with U[1] being > first column and U[2] second column) > > is it > > y_t = theta_(t-1)y_t-1 + .... + theta_t-3 y_t-3 + intercept + U[1]_t + > psi[1]_t-1 U[1]_t-1 + psi[1]_t-2 U[1]_t-2 + ....+ psi[2]U[2]_t-2 + > e_t + phi_t-1 e_t-1 + phi_...

...nverse of a Toeplitz matrix. I am looking for a so to say "multivariate" version of that. I found the Block Levinson algorithm in Matlab, but didn't find any version of it in R. My problem is part of a bigger problem, which is first computing the log-likelihood of the observations Y_T=(Y_1, ..., Y_T) of an n-dimensional time-series (Y_t) and second, finding an approximation of the MLE by using e.g. the BFGS algorithm. As this algorithm does not function properly (no convergence), I thought that maybe the inversion of the big covariance matrix EY_T Y_T' may be a source a...

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 -------------- next part -------------- An HTML attachment was scrubbed...

Hi, I'm trying to estimate the parameters of a state space model of the following form measurement eq: z_t = a + b*y_t + eps_t transition eq y_t+h = (I -exp(-hL))theta + exp(-hL)y_t+ eta_{t+h}. The problem is that the distribution of the innovations of the transition equation depend on the previous value of the state variable. To be exact: y_t|y_{t-1} ~N(mu, Q_t) where Q is a diagonal matrix with elements equal...

Dear R-help, I have been trying really hard to generate the following vector given the data (x) and parameter (alpha) efficiently. Let y be the output list, the aim is to produce the the following vector(y) with at least half the time used by the loop example below. y[1] = alpha * x[1] y[2] = alpha^2 * x[1] + alpha * x[2] y[3] = alpha^3 * x[1] + alpha^2 * x[2] + alpha * x[3] ..... below are

Dear R-Helpers, I am looking for ways to assess quality of a predictor of variance of a random variable. Here a two related, but yet distinct, setups. 1. I observe y_t, t=1,...,T which is normally distributed with unknown variance v_t (note that the variance is time-dependent). I have two "predictors" for v_t, dubbed v1_t and v2_t, and I want to tell which predictor is better. Here better is to be defined, but intuitively it is thought to be analogous t...