search for: endogen

...n US market where it has 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,D...

Hi there, as a student sociology, I'm starting to learn about SEM. The course I follow is based on LISREL, but I want to use the SEM-package on R parallel to it. Using LISREL, I found it to be very usable to be able to see the total direct and total indirect effects (standardized and unstandardized) in the output. Can I create these effects using R? I know how to calculate them

Hi there, I am really new to statistics in R and statistics itself as well. My situation: I ran a lot of OLS regressions with different independent variables. (using the lm() function). After having done that, I know there is endogeneity due to omitted variables. (or perhaps due to any other reasons). And here comes the Hausman test. I know this test is used to identify endogeneity. But what I am not sure about is: "Can I use the Hausman test in a simple OLS regression or is it only possible in a 2SLS regression model?&qu...

...have a function to estimate with gam (in the mgcv package), y=s(x1)+s(x2)+XB where X is a vector of exogenous variables and x1 and x2 are explanatory variables assumed parametric linear functions of X and other exogenous variables Z. Is there a way to evaluate this equation with gam, allowing for endogeneity? If not, is there another semiparametric/nonparametric evaluation function where endogeneity can be programed more easily? I really appreciate any help I can get with this. Thank you very much. Michael Milligan Doctoral Candidate University of New Mexico

Dear R-Users, I am using pgmm in the plm package to estimate a dynamic models with panel data. Besides the lagged dependent variable, I also have some other endogenous variables. Does the pgmm have an argument that allows me to specify these endogenous variables and their instruments? I didn't find this argument in the description and online. Thank you very much for your help! Ruiqing [[alternative HTML version deleted]]

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 used synonymous with a continuous variable, or pe...

Hi all! Does anyone know if a vector autoregression package is avaialable that allows binary variables as part of the endogenous system? I'm looking for something along the lines of what is implemented in "Dynamic Forecasts of Qualitative Variables: A Qual VAR Model of US Recessions" by Michael Dueker, 2003, Fed Reserve Bank of St. Louis. Another possibility is the autoregressive conditional hazard model b...

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...

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 investment function I would like to use for estimating my endogenous capacity util...

...ory scale about how much the respondent likes the American Flag and Y2 might be how much the respondent likes the Republican Party in America. By the usual simultaneous equation argument, one should not simply run 2 polr polr (Y1 ~ Y2 + X1 +X2) and polr(Y2 ~ Y1 + X1 + X2) because Y1 and Y2 are endogenous. Where does the problem arise? Thinking back to the theoretical model, there are unmeasured scale variables y1* and y2* that are determined by y1* = b0 + b1 * y2 + b2 * X1 + b3 * X2 + e1 and y2* = c0 + c1 * y1 + c2 * X1 + c3 * X2 + e2 y1* and y2* are not observed, we see only the categorical...

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 regression. Is there a simple way to do...

Hi, I have two sets of data that I would like to put into a data frame. But since they have different length, I am not sure how to do this. Here is an example of my data: data set one: date growth 1/1/2007 10 1/2/2007 10.2 1/3/2007 10.4 1/4/2007 10.6 data set two: date growth 1/1/2007 22 1/2/2007 22.5 1/4/2007 22.4 I would like to combine the two data sets and

Hello, I have a set of 100 variables with 1560 observations. I did an O.L.S regression of three of these variables on a fourth. But there are problems of endogeneity... So I look in my dataset for instruments to do an IV. I can't find a good instrument because their correlation with my endogeneous variables are too low. But I see that when I create a combined variable composed of 12 variables of the dataset my correlation is much stronger. Can I use suc...

Greetings, I am working in the sem package on a model with 3 exogenous variables (2 are nominal-categorical), and 4 endogenous, continuous variables. To use sem with the nominal variables, I created dummy variables. Now, in my sem output I have estimates for path coefficients for the relationship between each level of the nominal variables and the endogenous variables they are associated with. However, what I am...

...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) { G <- ma...

Hello, I see on the systemfit manual that you can estimate one-equation IV - I have a variable, and need to test if it's endogeneous, but do not need to estimate a system. Does anyone have any examples of this? Do you just run OLS with the endogenous variable, and then run a Hausmann to test endogeneity of OLS resid. vs. IV resid? Thanks in advance, DM [[alternative HTML version deleted]]

Dear all, I am using the Instrumental Variable approach to estimate the causal effects of TWO endogenous variables in a Mendelian Randomization study. As long as point estimation is concerned, I have no problem: both "ivreg" in library "AER" and "tsls" in library "sem" do the job perfectly. The problems begin when I try to obtain confidence intervals for th...

...lt;- -2; b22 <- 1 # eq.3 b30 <- 1; b31 <- 5; b32 <- 2 # exogenous variables x1 <- runif(min=-10,max=10,N) x2 <- runif(min=-5,max=5,N) # residual covariance matrix sigma <- matrix(c(2,1,0.7,1,1.5,0.5,0.7,0.5,2),3,3) # residuals r <- mvrnorm(N,mu=rep(0,3), Sigma=sigma) # endogenous variables y1 <- b10 + b11 * x1 + b12*x2 + r[,1] y2 <- b20 + b21 * x1 + b22*x2 + r[,2] y3 <- b30 + b31 * x1 + b32*x2 + r[,3] y <- cbind(y1,y2,y3) # matrix of endogenous x <- cbind(1,x1, x2) # matrix of exogenous #### MODEL ESTIMATION ### # build the big X matrix needed for G...

...ethod = "max.eigen") # data ... timeseries data as list (each entry is a matrix of a subsystem of variables, # if exo.var=TRUE the last entry are exogeneous variables) # tw ... time window, vector of start and end point # p ... scalar/vector of endogenous lags, (N+1)x1 # q ... scalar/vector of weakly exogeneous lags, (N+1)x1 # r ... vector of cointegrating relations # weight ... weight matrix of dimension (N+1)x(N+1) # case ... scalar/vector of cases ("I" to "V"), (N+1)x1 # endo ... list...

...d76r67d48d918af57244 at mail.gmail.com> Content-Type: text/plain First I have to say I am sorry because I have not been so clear in my previous e-mails. I will try to explain clearer what it is my problem. I have the following model: lnP=Sc+Ag+Ag2+Var+R+D In this model the variable Sc is endogenous and the rest are all objective exogenous variables. I verified that Sc is endogenous through a standard Hausman test. To determine this I defined before a new instrumental variable, I2. Also I detected through a Breusch Pagan Test a problem of heteroskedasticity. With the intention to avoid the...