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...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 normal utilization...

Hello everybody, I've a question about "autoregressive Regressionmodels". Let Y[1],.....,Y[n], be a time series. Given the model: Y[t] = phi[1]*Y[t-1] + phi[2]*Y[t-1] + ... + phi[p]*Y[t-p] + x_t^T*beta + u_t, where x_t=(x[1t],x[2t],....x[mt]) and beta=(beta[1],...,beta[m]) and u_t~(0,1) I want to estimate the coefficients phi and beta. Are in R any functions or packages for "autoregressive Regressionmodel" with special summaries?. I'm not meaning the function "ar". Example...

...tate 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 function in constant terms. The...

...er, 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 would be u_t = -Kx_n, where x_n is the Kalman filter state estimate. These inputs would be entered as such x_t = Ax_t-1 + Bu_t-1 + Ge_t. Is l.SS in the dse1 package the correct parametrization of the Kalman filter? Thank you very much, Todd Remund

...) = Pois (exp(Xb + e)) > >I don't think I ever understood very well why this leads to a NB >model. Maybe that's where I need to study. > >Nevertheless, where can I go if I start with that theory, but >the e are not independent, say they are MA(1) > >e_t = g*e_{t-1} + u_t > >and u_t is Normal(0,sigma^2). > >Should I just write out a big log likelihood function and use >R's optim to fit it? > >It seems like I'm missing out on something by going that route, >though. > >-- >Paul E. Johnson email: pauljohn a...

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

Hi, I am trying to write the Realized GARCH model with order (1,1) The model can be describe bellow: r_t = sqrt( h_t) * z_t logh_t = w + b*logh_(t-1) + r*logx_(t-1) logx_t = c + q*logh_t + t1*z_t +t2*(z_t ^2 -1) + u_t and z follow N(0,1) , u follow N(0, sigma.u^2) But I'm troubled with the simulation check for my code. After I simulate data from the model and estimate the data, I can't get precise estimation for my setting parameters. This is my simulation code: ======================= sim<-funct...

...-39,10 +39,9 @@ > * C: render target (color), blendable only on nvc0 > * D: scanout/display target, blendable > * Z: depth/stencil > - * V: vertex fetch > * I: image / surface, implies T > */ > -#define U_V PIPE_BIND_VERTEX_BUFFER > +#define U_V 0 > #define U_T PIPE_BIND_SAMPLER_VIEW > #define U_I PIPE_BIND_SHADER_BUFFER | PIPE_BIND_SHADER_IMAGE | > PIPE_BIND_COMPUTE_RESOURCE > #define U_TR PIPE_BIND_RENDER_TARGET | U_T > @@ -103,10 +102,7 @@ > (NV50_TIC_TYPE_##t1 << NV50_TIC_0_TYPE1__SHIFT) | \ >...

...llium/drivers/nouveau/nv50/nv50_formats.c @@ -39,10 +39,9 @@ * C: render target (color), blendable only on nvc0 * D: scanout/display target, blendable * Z: depth/stencil - * V: vertex fetch * I: image / surface, implies T */ -#define U_V PIPE_BIND_VERTEX_BUFFER +#define U_V 0 #define U_T PIPE_BIND_SAMPLER_VIEW #define U_I PIPE_BIND_SHADER_BUFFER | PIPE_BIND_SHADER_IMAGE | PIPE_BIND_COMPUTE_RESOURCE #define U_TR PIPE_BIND_RENDER_TARGET | U_T @@ -103,10 +102,7 @@ (NV50_TIC_TYPE_##t1 << NV50_TIC_0_TYPE1__SHIFT) | \ (NV50_TIC_TYPE_##t2 <&lt...

From: Ben Skeggs <bskeggs at redhat.com> Signed-off-by: Ben Skeggs <bskeggs at redhat.com> --- src/gallium/drivers/nouveau/nv50/g80_defs.xml.h | 279 ++++++++++++++++++++++++ 1 file changed, 279 insertions(+) create mode 100644 src/gallium/drivers/nouveau/nv50/g80_defs.xml.h diff --git a/src/gallium/drivers/nouveau/nv50/g80_defs.xml.h

I have data that looks like this: > df1 group id 1 red A 2 red B 3 red C 4 blue D 5 blue E 6 blue F I want a list of the groups containing vectors with the ids. I am avoiding subset(), as it is only recommended for interactive use. Here's what I have so far: df1 <- data.frame(group=c("red", "red", "red", "blue",