similar to: loops in R help me please

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 price dynamics of common

Dear R-users, I am trying to estimate a state space model of the form (1) b_t = G * b_t-1 + w_t w_t ~ N(0,W) (2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V) (Hamilton 1984: 372) In particular my estimation in state space form looks like (3) a3_t = 1 * a3_t-1 + w_t w_t ~ N(0,W) (4) g_t = (a1, a2) * (1, P_t)' + u_t * a3_t + v_t v_t ~ N(0,V) where g_t is the

  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

Dear all, I'd like to discuss about a possible bug in function StructTS of stats package. It seems that the function returns wrong value of the log-likelihood, as the added constant to the relevant part of the log-likelihood is misspecified. Here is an simple example: > data(Nile) > fit <- StructTS(Nile, type = "level") > fit$loglik [1] -367.5194 When computing the

Hi, I am trying to figure how to use dlm with constant terms (possibly time-dependent) added to both equations y_t = c_t + F_t\theta_t + v_t \theta_t = d_t + G_t\theta_{t-1} + w_t, in the way that S-PLUS Finmetrics does? Is there any straightforward way to transform the above to the default setup? Thanks, Tsvetan -------------------------------------------------------- NOTICE: If received in

I am having a problem with the package SSPIR. The code below illustrates it. I keep getting the message: "Error in y - f : non-conformable arrays." I tried to tweak the code below in many different ways, for example, substituting rbind for cbind, and sometimes I get a different error message, but I could not find a variation of this code that would work. Any help will be greatly

Hi all, I'm working on a term structure estimation using state-space modeling for 1, 2 and 3 factor models. When I started to read the functions on R, I got to the function ss on the library sspir. From what I understood this function is similar to SsfFit from S-PLUS. But for my models purpose there is something left to be desired. Its formulation follow these equations: *Y_t = F_t^T *

Dear all, I am currently trying to use the "dlm" package for Kalman filtering. My model is very simple: Y_t = F'_t Theta_t + v_t Theta_t = G_t Theta_t-1 + w_t v_t ~ N(0,V_t) = N(0,V) w_t ~ N(0,W_t) = N(0,W) Y_ t is a univariate time series (1x1) F_t is a vector of factor returns (Kx1) Theta_t is the state vector (Kx1) G_t is the identity matrix My first

Dear r-users! I have another question regarding the dlm package and I would be very happy if someone could give me a hint! I am using the dlm package to get estimates for an endogenous rate of capacity utilization over time. The general form of a state space model is (1) b_t = G * b_t-1 + w_t w_t ~ N(0,W) (2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V) (Hamilton 1984: 372) The

Dear R Users, I am new to state-space modeling. I am using SSPIR package for Kalman Filter. I have a data set containing one dependent variable and 7 independent variables with 250 data points. I want to use Kalman Filter for forecast the future values of the dependent variable using a multiple regression framework. I have used ssm function to produce the state space (SS)

I tried the version you used, too. the resulting executable was still broken. I guess the reason is due to fastcall on function pointers, which Clang does not recognize. Consider the following snippet. #include <stdio.h> void __attribute__((fastcall)) f(int i) { printf("%d\n", i); } typedef void (*__attribute__((fastcall)) f_t)(int i); //typedef void

I'm trying to use the following command. arima (x, order = c(p,d,q), seasonal =list(order=c(P,D,Q), period=s) How 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

Dear all, I am trying to write an R function that can estimate Transfer functions *with additive noise* i.e. Y_t = \delta^-1(B)\omega(B)X_{t-b} + N_t where B 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

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

> On May 12, 2018, at 9:42 AM, malika yassa via R-help <r-help at r-project.org> wrote: > > > hello > for exampl, i have this programme > # Generating data which are right truncated > library(DTDA) > library(splines) > library(survival) > n<-25 > X<-runif(n,0,1) > V<-runif(n,0.75,1) > for (i in 1:n){ > while (X[i]>V[i]){ >

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

hello for exampl, i have this programme # Generating data which are right truncated library(DTDA) library(splines) library(survival) n<-25 X<-runif(n,0,1) V<-runif(n,0.75,1) for (i in 1:n){ while (X[i]>V[i]){ X[i]<-runif(1,0,1) V[i]<-runif(1,0.75,1) }} res<-lynden(X=X,U=NA, V=V, boot=TRUE) attach(res) temps = time M_i = n.event L_t = res

Hi, I have the following type of data: 86 subjects in three independent groups (high power vs low power vs control). Each subject solves 8 reasoning problems of two kinds: conflict problems and noconflict problems. I measure accuracy in solving the reasoning problems. To summarize: binary response, 1 within subject var (TYPE), 1 between subject var (POWER). I wanted to fit the following model:

Hey guys, i want to calculate the continuasly compounded returns for stock prices. Formula for CCR: R_t = ln(P_t/P_{t-1})*100 With R: First i have to modify the vectors, so that they have the same length and we start at the second observation. log(GOOG1[-1]/GOOG1[1:length(GOOG1)-1])*100 That does work with normal vectors. My Questions: 1) I want to use this for stock prices. so i

my teachers doesnt understand R and I don't know how to use SAS. Anyone interested in translating my codes to test whether your SAS codes are as good as R??? I can test it on SAS codes once you have translated it .... regards:working: -- View this message in context: http://old.nabble.com/Translation-from-R-codes-to-SAS.-tp26486117p26486117.html Sent from the R help mailing list