search for: endogeneous

Hi List l am interested in developing price model. I have found a research paper related to price model of corn in 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

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

Hello, I have a question. Suppose that I 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

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!

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

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

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

A student brought this question to me and I can't find any articles or examples that are directly on point. Suppose there are 2 ordinal logistic regression models, and one wants to set them into a simultaneous equation framework. Y1 might be a 4 category scale about how much the respondent likes the American Flag and Y2 might be how much the respondent likes the Republican Party in America.

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

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 such a combined variable to estimate my equation with 2SLS, or GMM. -- View this message in context: http://r.789695.n4.nabble.com/IV-estimatio...

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

Hi, I have the following problem. It has two parts. 1. I need to calculate the stationary probabilities of a Markov chain, eg if the transition matrix is P, I need x such that xP = x in other words, the left eigenvectors of P which have an eigenvalue of one. Currently I am using eigen(t(P)) and then pick out the vectors I need. However, this seems to be an overkill (I only need a single

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

Dear helpers, I wrote a code which estimates a multi-equation model with generalized least squares (GLS). I can use GLS because I know the covariance matrix of the residuals a priori. However, it is a bit slow and I wonder if anybody would be able to point out a way to make it faster (it is part of a bigger code and needs to run several times). Any suggestion would be greatly appreciated. Carlo

Hi, I am brand new to R and not familiar with the language, though I have been reading the manuals and making some slow going progress. I am working with some source code from a Global Vector Auto -Regressive program written by Ranier Puhr from the R-forge group. I need help interpreting the processes of the following code. I am going to post in parts since it's pretty long: GVAR

Guillermo, I am dropping most of your mail because my answer is very generic. First, why doesn't it work as you tried it: technically speaking, coeftest() and the like expect to be feed an lm or a glm object and for this reason won't accept the result of systemfit(), which is a much different object. I suppose the same goes for the rest. Second, what can you do: I'd do at least one