similar to: alpha in Holt-Winters method

Hello, I asked this question on the r-finance list server and didn't get a reply. Thought I would try here to. I am trying to deseasonalize some financial time series data and I wanted some feedback on the best methods for doing this. I found two Centered Moving Average and Holt-Winters. Which is better and/or more appropriate for financial time series data in your opinion? I understand

Dear Members, I have been searching for a package in R which can handle multiple seasonality suggested by taylor(2003). It will be great help if anybody has used this on R before (i.e. which package). Thanks in Advance. Best Regards Atul Malik [[alternative HTML version deleted]]

Hi, how can I read, using for example read.table() or scan(), just one column from a text file that has more columns without any header? Thanks, bye.

I'm a very recent user of R. I have been adapting my Splus programmes and I found only one (important) problem. There exists no function "nlmin" in R and its substitute, "nlm", does not work well with my kind of problems, sometimes no achieving convergence, other tines "converging" to impossible values. My models are highly nonlinear and are to be estimated by

Dear all, I am using the functions 'optim' and 'nlminb'. For both, you can provide a function which computes the gradient of the objective function (to enhance speed and precision). In my case, both the objective function and the gradient take time to be computed and share many common computations (similar matrix, products, etc...). Therefore, I have to compute these

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

Hello again Is there any way (or an alternative non-linear minimiser) that arguments to the function called in nlm can be passed in version 0.62.4? Like (I believe) nlmin in a well known other program or optimise in R. Do we use global variables? Shurely not! \John -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Dear R users, I am trying to minimise a function using "nlm". I am getting the following error message: "Error: function is too long to keep source" The function is really very long (about 100 A4 pages). Is there anything I could do to solve this problem? At the moment I am using "nlmin" in S-Plus with no problems but I'd prefer to use R. Thank you very

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

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:

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 <-

I'm trying to find a good open source software to do sales forecasting using Holt Winters and Box Jenkins time series algorithm. Somebody pointed me that R is the best open source available for statistical computing. Are there functions to do Holt Winters and Box Jenkins time series prediction in R? If there is none, can some one point me a good GNU/freeware to do the sales forecasting using

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

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

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

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

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

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 --------------

Good morning to all, I should find the zero of a specific function with respect to a vector of arguments. Does it exist something similar in R? Thank you very much, Enrico Foscolo

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