similar to: Ex ante forecasting from structural equation models (SEM package)

Hi Frances, I have not touched the system.fit package for quite some time, but to solve your problem the following two pointers might be helpful: 1) Recast your model in the revised form, i.e., include your identity directly into your reaction functions, if possible. 2) For solving your model, you can employ the Gau?-Seidel method (see https://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method).

Frances, I would not advise Gauss-Seidel for non linear models. Can be quite tricky, slow and diverge. You can write your model as a non linear system of equations and use one of the nonlinear solvers. See the section "Root Finding" in the task view NumericalMathematics suggesting three packages (BB, nleqslv and ktsolve). These package are certainly able to handle medium sized models.

Who was speaking about non-linear models in the first place??? The Klein-Model(s) and pretty much all simultaneous equation models encountered in macro-econometrics are linear and/or can contain linear approximations to non-linear relationships, e.g., production functions of the Cobb-Douglas type. Best, Bernhard -----Urspr?ngliche Nachricht----- Von: Berend Hasselman [mailto:bhh at xs4all.nl]

> On 13 Jul 2017, at 12:55, Pfaff, Bernhard Dr. <Bernhard_Pfaff at fra.invesco.com> wrote: > > Who was speaking about non-linear models in the first place??? > The Klein-Model(s) and pretty much all simultaneous equation models encountered in macro-econometrics are linear That's really not true. Klein model is linear but Oseibonsu did not say that explicitly. "Klein

Hello, I have estimated a simultaneous equation model (similar to Klein's model) in R using the system.fit package. I have an identity equation, along with three other equations. Do you know how to explicitly identify the identity equation in R? I am also trying to forecast the dependent variables in the simultaneous equation model, while incorporating the identity equation in the

Hello, Is there a package or sample code that shows how to do ex ante forecasts with a macroeconomic structural equations model? I looked at the "sem" package, which lets you estimate e.g. Klein's model, but I'm not sure how to make simulations using the full set of equations, including the identities. Thank you, Ronaldo Carpio rncarpio at yahoo.com

Hi, I would like to estimate a VAR of the form: Ay_t = By_t-1 + Cy_t-2 + ... + Dx_t + e_t Where A is a non-diagonal matrix of coefficients, B and C are matricies of coefficients and D is a matrix of coefficients for the exogenous variables. I don't think the package {vars} can do this because I want to include contemporaneous cross-variable impacts. So I want y1_t to affect y2_t and I

Hello, I've been unsuccessfully trying to reproduce a sem from Grace et al. (2010) published in Ecological Monographs: http://www.esajournals.org/doi/pdf/10.1890/09-0464.1 The model in question is presented in Figure 8, page 81. The errors that I've been getting are: 1. Using a correlation matrix: res.grace <- sem(grace.model, S = grace, N = 190) Warning message: In sem.default(ram

At 2013-08-12 01:18:30,"Tobias Grosser" <tobias at grosser.es> wrote: >On 08/10/2013 06:59 PM, Star Tan wrote: >> Hi all, >> >> I have evaluated Polly's performance on LLVM test-suite with latest LLVM (r188054) and Polly (r187981).  Results can be viewed on: http://188.40.87.11:8000. > >Hi Star Tan, > >thanks for the update. >

Hi all, I am attempting to learn my way through the sem package by constructing a simple structural model for some of my data on bird diversity, abundance, and primary productivity. I have constructed a covariance matrix between these variables as per the following: >S_matrix = matrix(c( >+ 0.003083259, 0, 0, >+ 0.143870284, 89.7648490, 0, >+ 0.276950919,

Hi R-helpers, I'm new to time series modelling, but my requirement seems to fall just outside the capabilities of the arima function in R. I'd like to fit an ARMA model where the variance of the disturbances is a function of some exogenous variable. So something like: Y_t = a_0 + a_1 * Y_(t-1) +...+ a_p * Y_(t-p) + b_1 * e_(t-1) +...+ b_q * e_(t-q) + e_t, where e_t ~ N(0, sigma^2_t),

On 10/05/2010 09:42 AM, hamed hamzehi wrote: > Hi > yes, I'm asking for any advice, I want to implement multithreaded code > generator in LLVM. > tnx Hi, this generally depends which kind of code you want to multithread, because generally this is a difficult problem. However, if you limit yourself for the moment to loops that fit into the polyhedral model, you can take

Hi all, I have evaluated Polly's performance on LLVM test-suite with latest LLVM (r188054) and Polly (r187981).  Results can be viewed on: http://188.40.87.11:8000. There are mainly five new tests and each test is run with 10 samples: clang (run id = 27):  clang -O3 pollyBasic (run id = 28):  clang -O3 -load LLVMPolly.so pollyNoGen (run id = 29):  pollycc -O3 -mllvm -polly-optimizer=none

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

On 08/10/2013 06:59 PM, Star Tan wrote: > Hi all, > > I have evaluated Polly's performance on LLVM test-suite with latest LLVM (r188054) and Polly (r187981). Results can be viewed on: http://188.40.87.11:8000. Hi Star Tan, thanks for the update. > There are mainly five new tests and each test is run with 10 samples: > clang (run id = 27): clang -O3 > pollyBasic (run id =

Dear list, for a data structure like in df: set.seed(100) Treatment<-rep(c("Nitrogen","Carbon", "Sulfur"),each=9) week<-rep(c(1,5,9),3,each=3) genes<-rep(c("18s", "EF1b", "NR"),9) copies<-rnorm(27, 1000000,400000) df<-data.frame(Treatment,week,genes,copies) i wrote this code for a xyplot: library(lattice)

I am using ggplot and posted this question at that helplist. It was suggested that I try a more general R-help list for a possible solution to this problem. Within ggplot, I am using geom_area with red and blue and expect where they overlap should be purple. But instead, it's dark red. Playing with alpha and with different colors doesn't seem to solve the problem. Here's a very

Dear Helplist: I am trying, unsuccessfully, to rename levels of a factor in a dataframe. The dataframe consists of two factor variables and one numeric variable as follows: Factor Site has 2 levels AB and DE, factor Fish has 30 levels, 15 associated with each Site e.g. 1-1, 1-2,.....2-1, 2-2.... I am trying to rename the levels of factor Site from AB to Fw and DE to Est while keeping them as

Good morning to you all, Sorry for taking your time from your research and teaching schedules.   If you have a non-stationary univariate time Series data that has the transformation: Say; l.dat<-log (series) d.ldat<-diff (l.dat, differences=1) and you fit say arima model. predit.arima<-predict (fit.series, n.ahead=10, xregnew= (n+1) :( n+10)) How could I re-transform

Dear Helplist I have a dataframe that holds the Southern Oscillation Index over the last few years: R> soi[1:3,] Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 1993 -9 -10 -12 -24 -9 -18 -11 -18 -9 -15 -1 0 2 1994 -2 -1 -14 -26 -13 -12 -18 -20 -19 -16 -9 -15 3 1995 -4 -5 2 -19 -9 -3 4 -1 3 -2 0 -8 QUESTION: how do I coerce reshape() into giving me this: