search for: e_t

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 pr...

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 nlminb and maxLi...

...ing 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 the white noise series of the model. the documentation is totally vague when it comes to xreg. I hope it is like above :) Would appreciate any remarks or comments. Thanks in advance. Sincerely, Jose

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...

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

...rement 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), and with the variance specified by something like sigma^2_t = exp(beta_t * X_t), where X_t is my exogenous variable. I would be very grateful if somebody could point me in the direction of a library that could fit this (or a similar) model. Thanks, James Kirkby...

...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 the white noise series of the model. > > the documentation is totally vague when it comes to xreg. I hope it is > like above :) > > Would appreciate any remarks or comments. Thanks in advance. > > Sincerely,...

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 [[alternative HTML version deleted]]

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...

...eHuron,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

...ning time variable. Fitting y = a + bt + e, I get the estimates of a and b as a = 9.12 and b = 2.07. So using these estimates I obtain y_fit = c(11.19,13.26,15.33,17.40,19.47,21.54,23.61,25.68,27.75,29.82,31.89,33.96, 36.03,38.10, 40.17,42.24,44.31,46.38,48.45,50.52)  # these are fitted values. e_t = (y - y_fit)   # dif between the observed y and fitted value of corresponding y > e_t  [1]   4.72  -3.46   1.83  -0.72  -3.94   1.12   7.40  [8] -17.06  18.07 -18.85  13.57  -5.27   0.72  -0.35 [15]   1.01   0.43   1.74  -2.68   4.63  -2.96 # We define e_t1 = c(-3.46,1.83,-0.72,-3.94,1.12,...

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... URL: https...

...es package--the funciton garchFit and garchOxFit if adding a regression to the mean formula, how to estimate the model in R? using garchFit or garchOxFit? For example, Observations is {x,y}_t,there may be some relation between x and y. the model is y_t=gamma0 + *gamma1*x_t*+psi*e_{t-1}+e_t the gamma1*x_t is regression. e_t=sqrt(h_t)*N(0,1) h_t=alpha0+alpha1*e_t^2+beta*h_{t_1}~~~~~~~GARCH(1,1). I didn't know how to estimate the model using function garchFit or garchOxFit or other functions? because the argument in garchFit/garchOxFit is formular.mean=~arma(1,...

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?

Dear R users, I'm trying to carry out monte carlo integration of a posterior density function which is the product of a normal and a gamma distribution. The problem I have is that the density function always returns 0. How can I solve this problem? Here is my code #generate data x1 <- runif(100, min = -10, max = 10) y <- 2 * x1^2 + rnorm(100) # # # # # # # # Model 0 # # # # # # #

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

Hi All: I was looking for, but could not locate in the packages, or in the R archive searches if there exists an R implementation of Cuzick's test of trend. The test is described as follows: An extension of the Wilcoxon rank-sum test is developed to handle the situation in which a variable is measured for individuals in three or more (ordered) groups and a non-parametric test for trend

Hello, I am interested in estimating this type of random effects panel: y_it = x'_it * beta + u_it + e_it u_it = rho * u_it-1 + d_it rho belongs to (-1, 1) where: u and e are independently normally zero-mean distributed. d is also independently normally zero-mean distributed. So, I want random effects for group i to be correlated in t, following an AR(1) process. Any idea of how

...rs 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? >From the residuals,...

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Is there any reason why the keyboard poll routine (int 16h) in conio.inc checks fro a serial port. when pxelinux.0 is run under a vm - the keyboard does not register. which makes menus useless. Also the timer is broken. - -- regs MR E_T _______________________ \ \ \ OOHH I hate TYPOS \ \ \ ~~~~~~~~~~~~~~~~~~~~~~~ -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.2.3 (GNU/Linux) iD8DBQFA1lEYPBbX35pvTSgRAl80AKCT0yak8+GULQGXeDn+BDBUZSLJ0gCdHx6G C2a/9TciWCfL/nhv1pFnBVg= =DBmh ---...