search for: exogenous

Hello all, I would like to carry out a single-equation approach of the Error Correction Model such as Delta_y(t) = a + b*y(t-1) + c*x1(t-1) + d*x2(t-1) + e*delta_x1(t) + f*delta_x2(t) + epsilon(t) Where, a, b, c, d, e, f are coefficients to be estimated, y is the dependent variable, and x1, x2 are independent variables. For the single equation approach of ECM, there is a requirement of the

>From the help document on KalmanLike, KalmanRun, etc., I see the linear Gaussian state space model is a <- T a + R e y = Z' a + eta following the book of Durbin and Koopman. In practice, it is useful to run Kalman filtering/smoothing/forecasting with exogenous factor: a <- T a + L b + R e y = Z' a + M b + eta where b is some known vector (a function of time). Some other software like S-plus and Mathematica include the above exogenous factor. SsfPack by Koopman, etal. also has the factor built in the model to accommodate practical uses. So what...

Is there any package for Vector Auto-regressive with exogenous variable other than fastVAR? Because it is not able to solve my problem of not taking the base in the model. Please suggest some appropriate solution!!!! -- View this message in context: http://r.789695.n4.nabble.com/Is-there-any-package-for-Vector-Auto-regressive-with-exogenous-variable-other-t...

I've posted this before but have not been able to locate what I'm doing wrong. I cannot determine how the forecast is made using the estimated coefficients from a simple AR(2) model when there is an exogenous variable. Does anyone know what the problem is? The help file for arima doesn't show the model with any exogenous variables. I haven't been able to locate any documents covering this. I put together a simple example of an AR(2) model (no exogenous variables) and another example of an A...

Hi, I would like to use the sem package to perform a path analysis (no latent variables) with a mixture of 2 nominal exogenous, 1 continuous exogenous, and 4 continuous endogenous variables. I seek advice as to how to calculate the appropriate covariance matrix for use with the sem package. I have read through the polycor package, and am confused as to the use of "numeric" for the hetcor function. Is this...

...iables included in each VECM, # corresponds to numbers in ord # exo.var ... if TRUE strictly exogeneous variables are included in the model # d ... list showing which strictly exogeneous variables enter the subsystem equations # lex ... scalar/vector of lags of exogenous variables # method ... select cointegrating rank by max. eigenvalue ("max.eigen") or trace statistic ("trace") # ----- Set subsystems ----- cmodel <- list() N <- length(data)-1 # number of subsystems i=0,1,...,N dims...

...taken demand & supply forces into consideration. Following are the equation: Supply equation: St= a0+a1Pt-1+a2Rt-1+a3St-1+a5D1+a6D2+a7D3+U1 -(1) Where D1,D2,D3=Quarterly Dummy Variables(Since quarterly data are considered) Here, Supply equation has 1 endogenous (St) & 6 exogenous variables (P t-1,Rt-1,St-1,D1,D2,D3) Demand Side: Demand of corn is divided into 3 equations: Feed equation: Ft=b0+b1Pt+b2P(sm)t+b3Bt+b4COFt+b5Ht+a6D1+a7D2+a8D3+U2 -(2) here there are 2 endogenous variable(Ft, Pt) & 7 exogenous variables (P(sm)t,Bt,COFt,D1,D2,D3) Export equation:...

I am fitting a reduced form VAR model using VAR in the vars library. I have several endogenous variables, and two exogenous variables. I would like to explore the effects of a shock to one of the exogenous variables on one of the endogenous variables. Using irf in the vars library only calculates the irf for the endogenous variables, this is obviously by design, is there some theoretical restriction on why it is not pos...

Anyone know how to esimate parameters in the system: x[k]=Ax[k-1]+ B + Gv[k-1] y[k]=x[k]+Du[k]+Hw[k] a system with exogenous u[k] in the measurement eq., v,w are iid, both eq. are gaussian. Thanks, Oyvind --------------------------------- [[alternative HTML version deleted]]

Hello all I'm trying to run some path analysis in either sem or lavaan (preferably lavaan because I find its interface easier to use). Most of my variables are continuously distributed and fairly well-behaved but I have a single exogenous variable (sex) which is not continuously distributed. Preliminary model fitting suggests that there aren't any sex by (anything else) interactions. The two approaches to dealing with this that I've come up with are to code it as a numerical variable (1 & 2 for female and male) and run t...

Hello - Here's what I'm trying to do. I want to fit a time series y with ARMA(1,1) + GARCH(1,1), there are also an exogeneous variable x which I wish to include, so the whole equation looks like: y_t - \phi y_{t-1} = \sigma_t \epsilon_t + \theta \sigma_{t-1} \epsilon_{t-1} + c x_t where \epsilon_t are i.i.d. random variables \sigma_t^2 = omega + \alpha \sigma_{t-1}^2 + \beta

Hello, I have the similar issue in estimating a GARCH model with exogenous variables in the mean equation. Currently, to my understanding, the garch function in tseries package can handle univariate model, and garchFit in fGarch can handle ARMA specification. I wonder if there is any R function that can handle exogenous variables in estimating GARCH? Thank you a lot....

Hi, I am trying to fit all subsets for a vector autoregression with exogenous variables. I have been looking at the 'leaps' function but I not sure how to get it to work when lags for each variable are included in the model. I would be really appreciative if someone could provide some links to examples. Thanks in advance! -- View this message in context: http://...

Hello All, I'm looking to test a variable in a logit model (glm(..., binomial(link="logit"))) for exogeneity (endogeneity). At this point I am planning to try implementing Jeffery Grogger's "A Simple Test for Exogeneity in Probit, Logit, and Poisson Regression Models", Economic Letters, 1990. To do this, I need to be able to do an instrumental variables NLS

...gen(t(P)) and then pick out the vectors I need. However, this seems to be an overkill (I only need a single vector!) and takes a lot of time -- P is 1176 x 1176! Is there a faster way? 2. In fact, P has a structure: it comes from the solution of a discrete dynamic optimzation problem. There are exogenous (X) and endogenous (N) states, and I have a policy function X x N -> N, which gives the choice of the agent for any (x,n) in (X,N). X has an exogenous transition matrix. I use the following function to build the "global" transition matrix: globalTransition <- function(U, modelenv...

...kage). I have just encountered another problem and thought I'd post it to the list. In this case, Gretl and EViews give me similar estimations, but R is completely different. The EViews results and gretl results are below followed by the R results. The model is an ARIMA(0,1,2) with a single exogenous regressor (X). The same data set was used. Here are the estimations: EViews: Dependent Variable: DSPOT Method: Least Squares Date: 07/23/08 Time: 14:37 Sample (adjusted): 2 518 Included observations: 517 after adjustments Convergence achieved after 8 iterations White Heteroskedasticity-Consis...

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 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...

...esearch Associate Centre for Social and Economic Research on the Global Environment (CSERGE), School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ United Kingdom. email: c.fezzi at uea.ac.uk *************************************** Here is an example with 3 equations and 2 exogenous variables: ----- start code ------ N <- 1000 # number of observations library(MASS) ## parameters ## # eq. 1 b10 <- 7; b11 <- 2; b12 <- -1 # eq. 2 b20 <- 5; b21 <- -2; b22 <- 1 # eq.3 b30 <- 1; b31 <- 5; b32 <- 2 # exogenous variables x1 <- runif(min=-10,m...

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), and with the variance specified by something like sigma^2_t = exp(beta_t * X_t), where X_t is my exogenous variable. I would be very grateful if...

Dear R - helpers, I am using the urca package to estimate cointegration relations, and I would be really grateful if somebody could help me with this questions: After estimating the unrestriced VAR with "ca.jo" I would like to impose the rank restriction (for example rank = 1) and then obtain the restricted estimate of PI to be utilized to estimate the VECM model. Is it possible? It