search for: z_t

I am new in R. I can simulate Arma, using Arima.sim However, I want to simulate an Arima Model. Say (1-B)Zt=5+(1-B)at. I do not know how to deal with 5 in this model. Can any one could help me? Thank you very much! Regards, -- View this message in context: http://www.nabble.com/simulate-arima-model-tp23239027p23239027.html Sent from the R help mailing list archive at Nabble.com.

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 apprecia...

This is a time series\optimization rather than an R question : Suppose I have an ARMA(1,1) with restrictions such that the coefficient on the lagged epsilon_term is related to the coefficient on The lagged z term as below. z_t =[A + beta]*z_t-1 + epsilon_t - A*epsilon_t-1 So, if I don't have a facility for optimizing with this restriction, is it legal to set A to something and then Optimize just for the beta given the A ? Would this give me the same answer likelihood wise, of optimizing both jointly with the restric...

Hi, I am trying to write the Realized GARCH model with order (1,1) The model can be describe bellow: r_t = sqrt( h_t) * z_t logh_t = w + b*logh_(t-1) + r*logx_(t-1) logx_t = c + q*logh_t + t1*z_t +t2*(z_t ^2 -1) + u_t and z follow N(0,1) , u follow N(0, sigma.u^2) But I'm troubled with the simulation check for my code. After I simulate data from the model and estimate the data, I can't get precise estimati...

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) get(as.chara...

....fit <- arima(LakeHuron,order=c(1,0,1)) then the function predict() can be used for predicting future data with the model: LakeH.pred <- predict(Lake.fit,n.ahead=5) I can see the result LakeH.pred$pred and LakeH.pred$se but I did not see residual in predict function. If I have a model: [\ Z_t = Z_{t-1} + A + e_t + B*e_{t-1} \] How could I find $e_t$ dan $e_{t-1}$ ? Best, XY

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 ele...

Hi..   R Program is shown ARIMA output as below then SARIMA equation is be   (1 - 0.991B^{12})z_t + 43.557 = (1+0.37B)(1-0,915B^{12})a_t    But I try to calculate it by manual . It look like it 's big different from R sofeware,   I am not sure this equation is correct or not . PLS supoort me to confirm it   Arima Model ( 0,0,1)(1,0,1)   No Transformation Constant   >> 43.557 , t = 10...

...f the standard errors or provide code that explains the inner workings of this function. Thanks! Example of the model I am running: model1<- gls(Y~ X1I + X2 + X3 + X4, data=Dat1, correlation = corAR1(), method = "ML") My understanding of model errors: Y = b_0 + X1 b_1+ ...Xk b_k + Z Z_t =phi Z_{t-1) + e_t The residuals reported by GLS are the Z's, while the white noise terms are the e's. I cannot replicate the reported residuals using this approach. I also do not know how Z_0 should be calculated, i.e. what does the first step of this recursive procedure look like? >F...

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

...01566420 0.01565676 6 0.001263328 0.01574062 0.01573312 7 0.001263328 0.01582800 0.01582047 8 0.001263328 0.01590372 0.01589614 9 0.001263328 0.01598779 0.01598018 10 0.001263328 0.01606258 0.01605493 I am modelling this Y_t-mean=e_t=sigma_t*Z_t however, my question is ,the prediction for the return itself is the mean forecast? if this is the case my prediction for the price would be equal to (1+.001451401)*110.41 =110.57 but i think this is not a good prediction, because the volatility is not affecting so much , in addition the...

...l(f)) lines(lowess(x,y,f=f)) } } else { lms_summary(rslt) e_rslt$residuals n_length(e) beta_rslt$coef p_length(rslt$coef) R_rslt$R Q_left.solve(R, cbind(rep(1,length(x)),x)) h_as.vector((Q^2 %*% array(1, c(p, 1)))) h.res_(1 - h) z_e/h.res v1_e^2 z_t(Q * z) v.res_sum(v1) v1_(v.res - v1/h.res)/(n-p-1) dbeta_backsolve(R, z) si_sqrt(v1) xxi_diag(lms$cov.unscaled) bi_t(beta-dbeta) dfbetas_(t(coef(rslt)-t(bi)))/(si %o% xxi^.5) adfbetas_abs(dfbetas[,2]) p1_(99/18)*adfbetas*(adfbetas+1)^2+1 p1[adfbetas>2]_100...

...stat.ethz.ch/pipermail/r-devel/2009-February/052009.html I could not figure out whether there is a real bug in getQ0 or if this is due to some numerical instability. However, I tried to replace getQ0 in two ways. The first one is to compute first the covariance matrix of (X_{t-1},...,X_{t-p},Z_t,...,Z_{t-q}) and this is achieved through the method of difference equations (page 93 of Brockwell and Davis). This way was apparently suggested by a referee to Gardner et al. paper (see page 314 of their paper). Q0bis <- function(phi,theta){ ## Computes the initial covariance matrix fo...

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I have data that looks like this: > df1 group id 1 red A 2 red B 3 red C 4 blue D 5 blue E 6 blue F I want a list of the groups containing vectors with the ids. I am avoiding subset(), as it is only recommended for interactive use. Here's what I have so far: df1 <- data.frame(group=c("red", "red", "red", "blue",