search for: unemp

Hello, I'm using VAR models in R in order to obtain impulse responses of stock market shock on US economy. I have series of quarterly changes in real gdp, S&P 500 and quarterly level of unemployment for 1985 - 2012 period. My series are stationary. So I did all the steps below. However I don't understand what do irf function results mean. These are the cumulative orthogonal responses to sp variable shock but what is the value of this shock? If for example I have to assess the respon...

...producible example) from that site: # ------ begin R library(AER) library(lmtest) data("CollegeDistance") cd.d<-CollegeDistance simple.ed.1s<- lm(education ~ distance,data=cd.d) cd.d$ed.pred<- predict(simple.ed.1s) simple.ed.2s<- lm(wage ~ urban + gender + ethnicity + unemp + ed.pred , data=cd.d) # ------ end R This yields the following summary: summary(simple.ed.2s) Call: lm(formula = wage ~ urban + gender + ethnicity + unemp + ed.pred, data = cd.d) Residuals: Min 1Q Median 3Q Max -3.1692 -0.8294 0.1502 0.8482 3.9537 Coefficients:...

Dear all, I am trying to export my fixed effect results to Latex. I am using the plm package with the summary function. However, it does not look like apsrtable, stargazer, or any other package can accompany using the plm package. I am interested in a classic table with the coefficient in one row followed by the standard error in paranthesis in the next row and stars by the coefficient to show

...ry.plm function (demonstrated with the example from the ?plm documentation). library(plm) data("Produc", package="Ecdat") estimation_method<-"within" estimation_effect<-"individual" zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, index=c("state","year") , method=estimation_method , effect=estimation_effect ) summary(zz) #Error: object of type 'symbol' is not subsettable #But if I run zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, i...

Hello, I am trying to run a fixed effects panel regression on data containing 5 columns and 1,494 rows. I read the data in as follows: >drugsXX<-read.csv(file="C:\\Folder\\vX.X\\Drugs\\drugsXX_panel.csv", head=TRUE, sep=",") Verified it read in correctly and had a good data.frame: >dim(drugsXX) [1] 1494 5 >drugs XX produce expected data with correct column

...summary.plm function (demonstrated with the example from the ?plm documentation). library(plm) data("Produc", package="Ecdat") estimation_method<-"within" estimation_effect<-"individual" zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, index=c("state","year") , method=estimation_method , effect=estimation_effect ) summary(zz) Error: object of type 'symbol' is not subsettable But if I run zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, ind...

Hi all, what indicates the parameter theta in the summary of a random effect panel model estimated with the plm function? example: data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, model="random", data = Produc, index = c("state","year")) summary(zz) Effects: var std.dev share idiosyncratic 0.001454 0.038137 0.175 individual 0.006838 0.082691 0.825 theta: 0.8888 Thanks Alfonso

Hi all, I have estimated a random panel model using plm function. I have a question about the vector of resduals obtained with the object $residuals. example: data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, model="random", data = Produc, index = c("state","year")) res<-zz$residuals # vector of the residuals. the vector res is the sum of the idyosiyncratic (eit) and individual (ui) component or is only the idyosiyncratic (eit) component? Thanks Alfonso

...bgtest pbsytest pcdtest pdim [11] pdwtest phtest print pwartest pwfdtest [16] pwtest residuals terms update vcov see '?methods' for accessing help and source code > coef(zz) log(pcap) log(pc) log(emp) unemp -0.026149654 0.292006925 0.768159473 -0.005297741 I don't understand why coef(zz) succeeds but coef is not listed as a method. Right now, I'm contemplating this: zz1 < - try(coef(zz)) if (inherits(zz1, "try-error")) stop("Your model has no coef method") This see...

...or) bug either the command all.equal() or in the "plm" package. I demonstrate this using an example taken from the documentation of plm(): ====================================== R> data("Produc", package="plm") R> zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, + data=Produc, index=c("state","year")) R> all.equal(zz,zz) [1] TRUE Warning message: In if (length(target) != length(current)) return(paste("target, current differ in having response: ", : the condition has length > 1 and only the first element will be use...

...#39;zoo' The following object(s) are masked from package:base : as.Date.numeric > which seems reasonable. However, it fails running the included example in the help. > data("Produc", package="Ecdat") > zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, index=c("state","year")) Error in do.call("~", list(firstpart, as.name(i))) : object 'firstpart' not found In addition: Warning messages: 1: In if (length(formula) == 2) formula <- expand.formula(formula) : the condition has length > 1 a...

...#39;zoo' The following object(s) are masked from package:base : as.Date.numeric > which seems reasonable. However, it fails running the included example in the help. > data("Produc", package="Ecdat") > zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, index=c("state","year")) Error in do.call("~", list(firstpart, as.name(i))) : object 'firstpart' not found In addition: Warning messages: 1: In if (length(formula) == 2) formula <- expand.formula(formula) : the condition has length > 1 a...

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...pcdtest pdim > [11] pdwtest phtest print pwartest pwfdtest > [16] pwtest residuals terms update vcov > see '?methods' for accessing help and source code >> coef(zz) > log(pcap) log(pc) log(emp) unemp > -0.026149654 0.292006925 0.768159473 -0.005297741 > > I don't understand why coef(zz) succeeds but coef is not listed as a method. There is a coef.default method. (Also an S$ coef,ANY-method ) > > Right now, I'm contemplating this: > > zz1 < - try(coef(zz))...

Hello, I am trying to conduct a Hausman test to choose between FE estimators and RE estimators. When I try to run: library(plm) fixed <- plm(ROS ~ DiffClosenessC +ZZiele + AggSK + nRedundantStrecken + Degree + KantenGew + BetweennessC + SitzKappazitaet, data=Panel,index=c("id","time"),model="within") summary(fixed) fixef(fixed) random <-plm(ROS ~

Dear R-helpers, I have a very simple question and I really hope that someone could help me I would like to estimate a simple fixed effect regression model with clustered standard errors by individuals. For those using Stata, the counterpart would be xtreg with the "fe" option, or areg with the "absorb" option and in both case the clustering is achieved with "vce(cluster

...mented in 'plm' yet. It might well be feasible by applying vcovHAC{sandwich} to the time-demeaned data but I'm not sure; in this case, vcovHAC should be applied this way (here: the famous Munnell data, see example(plm)) > library(plm) > fm<-log(gsp)~log(pcap)+log(pc)+log(emp)+unemp > data(Produc) > ## est. FE model > femod<-plm(fm, Produc) > ## extract time-demeaned data > demy<-pmodel.response(femod, model="within") demX<-model.matrix(femod, > model="within") ## estimate lm model on demeaned data ## (equivalent > to FE, but...

Dear all, I've got a problem in fitting multilevel model in lme. I don't know to much about that but suspect that something is wrong with my model. I'm trying to fit: m1<-lme(X~Y,~1|group,data=data,na.action=na.exclude,method="ML") m2<-lme(X~Y+Z,~1|group,data=data,na.action=na.exclude,method="ML") where: X - dependent var. measured on a scale ranging from

...mented in 'plm' yet. It might well be feasible by applying vcovHAC{sandwich} to the time-demeaned data but I'm not sure; in this case, vcovHAC should be applied this way (here: the famous Munnell data, see example(plm)) > library(plm) > fm<-log(gsp)~log(pcap)+log(pc)+log(emp)+unemp > data(Produc) > ## est. FE model > femod<-plm(fm, Produc) > ## extract time-demeaned data > demy<-pmodel.response(femod, model="within") > demX<-model.matrix(femod, model="within") > ## estimate lm model on demeaned data > ## (equivalent to FE,...