search for: a_t

...is the backward shift operator, b is the delay and N_t is a noisy component that can be modelled as an ARMA process. The parameters to both the impulse response function and the ARMA noisy component need to be estimated simultaneously. I have got as far as being able to compute the residual noise, a_t. However, I am slightly confused about what to do next. Reading Box-Jenkins, 1976 (pp. 391) they state the following "....However, it seems simplest to work with a standard nonlinear least squares computer program in which the derivatives are determined numerically and an option is available...

I am trying to make the following Kalman filter equations work and therefore produce their graphs. v_t=y_t - a_t a_t+1=a_t+K_t*v_t F_t=P_t+sigma.squared.epsilon P_t+1=P_t*(1-K_t)+sigma.squared.eta K_t=P_t/F_t Given: a_1=0,P_1=10^7,sigma.squared.epsilon=15099, sigma.squared.eta=1469.1 I have attached my code,which of course doesnt work.It produces NAs for the Fs,Ks and the a. Can somebody tell me please what...

...can I write an estimated seasonal ARIMA model from the outputs. To be specifically, which sign to use? I know R uses a different signs from S plus. Is it correct that the model is: (1-ar1*B-ar2*B^2-...)(1-sar1*B^s-sar2*B^2s-....)(1-B)^d(1-B^s)^D X_t=(1+ma1*B+ma2*B^2+...)(1+sma1*B^s+sma2*B^2s+....) a_t For example: > m1=arima(koeps,order=c(0,1,1),seasonal=list(order=c(0,1,1),period=4)) > m1 Call: arima(x = koeps, order = c(0, 1, 1), seasonal = list(order = c(0, 1, 1), period = 4)) Coefficients: ma1 sma1 -0.4096 -0.8203 s.e. 0.0866 0.0743 Should the estimated model...

...Time Series" by Ruey S. Tsay, and I was succesfull, but I had to use "for" loop, which is quite slow. The loop is necessary, since you need to calculate recursive sequence. Is there a faster way to do this in R, without using loops? The model is such: r_t = \mu + \alpha_2 r_{t-2} + a_t a_t = \sigma_t\varepsilon_t \sigma_t^2 = \beta_1a_{t-1}^2+\beta_2\sigma_{t-1}^2+ 1_{\{a_{t-1}>0\}}(\gamma_0+ \gamma_1a_{t-1}^2+\gamma_2\sigma^2_{t-1}) It is asummed that \varepsilon_t are iid and normal with zero mean and variance one. The data given is r_t, and you have to estimate variables,...

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 a mean and variance model jointly. For example I might want to fit an AR(2)-GARCH(1,1) model i.e. r_t = constant_term1 + b*r_t-1 + c*r_t-2 + a_t where a_t = sigma_t*epsilon_t where sigma^2_t = constant_term2 + p*sigma^2_t-1 + q*a^2_t-1 i.e. R estimates a constant_term1, b, c, constant_term2, p, q TIA Aditya

  Hello, fellow R users,   I recently need to estimate a dynamic linear model in the following form:   For the measurement equation:   Y_t = F_t * a_t + v_t   where Y_t is the observation. It is a 1 by q row vector for each t. F_t is my forecasting variable. It is a 1 by p row vector. a_t is my state variable. It is a p by q MATRIX of parameters with each column of the matrix being regression coefficient of a random variable in Y_t. And v is a mu...

  Hi, All,   This is the first time I seriously use this package. However, I am confused that the result is quite unstable. Maybe I wrote something wrong in the code? So could anybody give me some hint? Many thanks.   My test model is really simple. Y_t = X_t * a_t + noise(V)，(no Intercept here) a_t = a_{t-1} + noise(W)   I first run the following code: (I shall provide data at the end of the mail)   BuildMod <- function(x){  return(dlm(   m0  = x[1],   C0  = x[2],   FF  = 1,   GG  = 1,   V   = x[3],   W   = x[4],   JFF = 1,   X   = X   )) } ModFit  <-...

...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 #{ X_t=a_t, X_{t-l_1}=a_{t-l_1}, X_{t-l_2}=a_{t-l_2}, ..., X_{t-l_k}=a_{t-l_k} } ---------------------------------------------------------------------------------- #{ X_{t-l_1}=a_{t-l_1}, X_{t-l_2}=a_{t-l_2}, ..., X_{t-l_k}=a_{t-l_k} } for any given sequence of a_t, a_{t-l_1}, a_{t-l_2}, ..., a_{t...

Dear R People: Suppose I want to estimate the parameters of the following AR model: (1 - phi_1 B - phi_2 B^2 - phi_9 B^9) x_t = a_t and I want to use the arima0 command from the ts library. How would I use the order subcommand, please? R Version 1.4.1 for Windows. Thanks! Sincerely, Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown 1 Main Street Houston, TX...

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

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

Hi, could anyone tell me how predict() predicts the new value(s), of a MA(1) arima-modell. its really easy to make it with an AR(1), knowing the last term, but how can i or R know the last error? It would also help if somebody could tell me how to find the "open" source of the function predict(). Thanks and sorry for my poor english. -- View this message in context:

Hello, Guys: I'm from China, my English is poor and I'm new to R. The first message I sent to R help meets some problems, so I send again. Hope that I can get useful suggestions from you warm-hearted guys. Thanks. I builded a multiplicative seasonal ARMA model to a series named "cDownRange". And the order is (1,1)*(0,1)45 The regular AR=1; regular MA=1; seasonal AR=0; seasonal

Hello, I am using {plm} to estimate panel models. I want to estimate a model that includes fixed effects for time and individual, but has a random individual effect for the coefficient on the independent variable. That is, I would like to estimate the model: Y_it = a_i + a_t + B_i * X_it + e_it Where i denotes individuals, t denotes time, X is my independent variable, and B (beta) is the coefficient on that random variable. I want both a coefficients to be estimated with fixed effects because I expect them to be correlated with Y, and B to be estimated using a random e...

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 = 10.09 MA         >> -0.37  , t = -...

Hola a tod en s, queremos hacer una comparación entre dos lugares muy alejados entre sí en relación a la temperatura de cada sitio usando medias horarias de un período determinado. Sólo hay medidas de un sensor en cada sitio y queremos saber si las diferencias son significativas o no entre sitios/curvas. Hemos usado un test de Mann?Whitney U con la función wilcox.test (paired=F) ya que los

...quire(sos) findFn('garch') Regards Liviu On Mon, Aug 23, 2010 at 5:59 AM, Aditya Damani wrote: > Hi, > > I want to fit a mean and variance model jointly. > > For example I might want to fit an AR(2)-GARCH(1,1) model i.e. > > r_t = constant_term1 + b*r_t-1 + c*r_t-2 + a_t > > where a_t = sigma_t*epsilon_t > > where sigma^2_t = constant_term2 + p*sigma^2_t-1 + q*a^2_t-1 > > i.e. R estimates a constant_term1, b, c, constant_term2, p, q > > TIA > Aditya > > ______________________________________________ > R-help at r-project.org mail...