search for: g_t

Hi All, I'm trying to do a Scatterplot (package: car), and add a line (just for reference). There is my code: #------------------------------------Code--------------------------------------------------- library("car") library("calibrate") G_T<-c("car","bike","boat") ave<-c(80,10,45) perf<-c(100,80,75) df2<-data.frame(G_T,ave,perf) scatterplot(ave ~ perf, data=df2,smoother=FALSE,xlim=c(0,100),ylim=c(0,100)) textxy(df2$perf,df2$ave...

...ion 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 investment function I would like to use for estimating my endogenous capacity utilization rate looks like (3) g_t = x[1] + x[2]*(u_t-un_t) + x[3]*r + v_t where g_t is the investment rate, r_t is the profit rate, u_t is the actual utilization rate and un_t is the 'normal' utilization rate which I take as endogenous (=time varying). x[i] are parameters. I'm particularly interested in this endogenous...

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 am looking for a Kalman filter that can handle a control input. I thought that l.SS was suitable however, I can't get it to work, and wonder if I am not using the right function. What I want is a Kalman filter that accepts exogenous inputs where the input is found using the algebraic Ricatti equation solution to a penalty function. If K is the gain matrix then the exogenous input

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 error, please destroy and notify sender. Sender does not intend...

...ers, 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 investment rate, P_t are profits and u_t is the utilization rate. As you can see, I would like to estimate the coefficient of the utilization rate in time-varying terms and all the other coefficients of the investment f...

...ock of each period t * the production function Ht(St, Xt) = alpha*St*Xt Ht the catch for each period t Xt fishing effort for each period t alpha parameter of boat productivity * the dynamic of the fish stock S(t+1) = S(t) + Gt - Ht I would like to modelise the following system: S_(t+1) = S_t + G_t - H_t G_t = r*S_t*(1-S_t / sbar) H_t = alpha * S_t * X_t S_1961 = S_0 I know only H_t on period (1961 - 1994) and X_t on the same period. I don't know r, sbar, alpha and S_0 (the initial level of the stock) (and of course S_t on this period) and I want to estimate this four parameters. I have...

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 challenge is to get the Maximum Likelihood estimators of V and W assum...

...e 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 * theta_t + v_t, v_t ~ N(0,V_t)* *theta_t = G_t * theta_{t-1} + w_t, w_t ~ N(0,W_t)* Actually I wanted to add these two functions (-logA and a) Y(t) = -log(A(t))/tau + (B(t)/tau) * X(t) + Error (measurement equation) X(t) = a(t) + b(t) * X(t-1) + Error (Transition Equation) Have anyone tried something like that? Bes...

Dear R listers, I have a very annoying problem using nlm(). I want to find the minimizer of my target function, if written in \LaTeX is f(\mu1,\mu2,\sigma1,\sigma2) = \sum_i^n( w_ig_t(z_i) ), where g_t(z) is a pdf of bivariate normal distribution and z_i is my samples. I cannot get the estimation result generated by nlm(), and I got the following errors " Error in nlm(foo, theta.start, hessian = TRUE) : non-finite value supplied by 'nlm' " f...

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