similar to: rank reduction method in time series analysis?

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

Dear Group, I am an economics student starting with PhD work in London. As preparation I would like to get to know R a little bit better. For Stata there are tons of books, however, can you recommend a book for R? I have some substantiated econometrics knowledge, so it should be more a how-to book. Best regards Thiemo --- Thiemo Fetzer, Economist http://freigeist.devmag.net

Hello, I am very new to R and Time Series. I need some help including R codes about the following issues. I' ll really appreciate any number of answers... # I have a time series data composed of 24 values: myinput = c(n1,n2...,n24); # In order to make a forecasting a, I use the following codes result1 = arima(ts(myinput),order = c(p,d,q),seasonal = list(order=c(P,D,Q))) result2 =

Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN

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

Dear R-Devel subscriber, first, let me express my thank to the R-Core team for the new release! I appreciate their efforts and time spent to enhance R. In accordance with the 'NEWS' file (see excerpt of it below), [... o Changed the environment tree to be rooted in an empty environment, available as emptyenv(). baseenv() has been modified to return an environment with emptyenv() as

Dear R-Devel subscriber, first, let me express my thank to the R-Core team for the new release! I appreciate their efforts and time spent to enhance R. In accordance with the 'NEWS' file (see excerpt of it below), [... o Changed the environment tree to be rooted in an empty environment, available as emptyenv(). baseenv() has been modified to return an environment with emptyenv() as

Hello all, I looking at package dse or vars or mAr I know how to simulate a VAR(p) process, my problem is that most of those processes are unstable (not weakly stationary). Do anybody know how to generate a random VAR (or VARMA even better) process that is weakly stationary? Thanks -- View this message in context: http://r.789695.n4.nabble.com/simulating-stable-VAR-process-tp4261177p4261177.html

Hi, I'm having especially hard time today and couldn't find any clue/answer through the internet. ?I hope you can help. I'm in a process of writing a script to estimate error correction model, and I was following an example in Bernhard Pfaff's Analysis of Integrated and Cointegrated Time Series with R. ?I have the following price data: > head(series,15) ?? ? ? ? ? PX_SETTLE

Hi R-Masters I need use a sequential approach in serie of cases, but may data is not normal. If data is normal distribution is very easy create analysis using likelihood ratio like of Wald test. But in my case I need use a non-parametric test (Mann-Whitney). I was use: RSiteSearch("sequential rank test") but not solve my problem. Do you know routine or package implement

I am currently trying to replicate the results I got from RATS for a panel regression. The codes in RATS looks like this: * Final equation for Office Cap Rate Spread * Regression, Panel Data preg(effects=time, method=random) CapRate # CapRate{1} RentCycle{1} VacancyChangeYTY InflationYTY RealGDPyty Just wonder what R package also allow me to have the options like (effects=time, method=random).

Hi all R users, I'm trying to replicate the same results that are given in a published article after been granted the same data that the authors use. I'm having problems to determine the cointegration rank of my data set using the Johnasen's trace test. This trace test is already programmed in the package ur.ca and can be found in the function ca.jo(). After I run the ca.jo()

Hi all, I am looking for low cost online education in statistics. I am thinking of taking online classes on time series analysis and cointegration, etc. Of course, if there are free video lectures, that would be great. However I couldn't find any free video lectures at upper-undergraduate and graduate level which formally going through the whole timeseries education... That's why I would

Hello to all- 1. When I run a regression which implements the augmented Dickey-Fuller test, I am confused about the names given to the regressors in the output. I understand what "xGE" stands for in a standard "lm" test involving an independent variable GE for instance, but if I lags and or differences are included in the model, what do the following "output" stand

Hello! I'm using the blrtest() function in the urca package to test cointegration relationships. Unfortunately, the hypothesis (restrictions on beta) specifies the same restriction on all cointegration vectors. Is there any possibility to specify different restrictions on the cointegration vectors? Are there any other packages in R using cointegration tests? Thanks and best regards. Dennis

Dear R Core Team, I am using package {urca} to do cointegration and estimate ECM model, but I have the following two problems: (1) I use ca.jo() to do cointegration first and can get the cointegration rank, alpha and beta. The next step is to test some restrictions on beta with blrtest(),bh5lrtest(), and bh6lrtest(). But none of them can add restrictions on all the cointegration

Howdy What I 'd like to analyze with a large data on building permits is to find time series effect of urban policy on buildings as well as cross-sectional effects in any. In 1990 the specialZone urban policy was introduced. I guess that the effects of this specialZone policy would be different from countys. There are counties that do not welcome this specialZone forced to design it. One of

As noted on the R-project web site itself ( www.r-project.org -> Manuals -> R Data Import/Export ), it can be cumbersome to prepare messy and dirty data for analysis with the R tool itself. I've also seen at least one S programming book (one of the yellow Springer ones) that says, more briefly, the same thing. The R Data Import/Export page recommends examples using SAS, Perl, Python, and

Hi, Is there any facility in R to perform a stepwise process on a model, which will remove any highly-correlated explanatory variables? I am told there is in SPSS. I have a large number of variables (some correlated), which I would like to just chuck in to a model and perform stepwise and see what comes out the other end, to give me an idea perhaps as to which variables I should focus on. Thanks

Hello! I need to use Kruskal-Wallis test and post-hoc test (Dunn's test) for my data. But when I searched around, I only found this function: kruskal.test. But nothing for Dunn's test. So I started to write one myself. But I do not know how to count ties in the data frame. I can use for loops but it seems long and unnecessary since the rank function actually knows the ties. So