similar to: ARCH LM test for univariant time series

Hi all, I am looking for study resources for (financial) time series? Hopefully I could find video lectures then it will reduce the learning curve. Thanks a lot! [[alternative HTML version deleted]]

Hi, All: FinTS version 0.2-7 is now available on CRAN. This version adds two new functions: * ArchTest to compute the Engle (1982) Lagrange multiplier test for conditional heteroscedasticity, discussed on pp. 101-102 of Tsay, with examples on those pages worked in the R script in "~R\library\FinTS\scripts\ch03.R", where "~R" is your local R installation directory.

Hi, All: FinTS version 0.2-7 is now available on CRAN. This version adds two new functions: * ArchTest to compute the Engle (1982) Lagrange multiplier test for conditional heteroscedasticity, discussed on pp. 101-102 of Tsay, with examples on those pages worked in the R script in "~R\library\FinTS\scripts\ch03.R", where "~R" is your local R installation directory.

Hi, I am trying to use the stl function in the ts package. It requires that the data is a univariant time series at the moment my data is in a vector. I have coerced it to a time series using.... crimets <- ts(crimeData) However, this does not work. Does anyone have any suggestions? Cheers, Sam. p.s. I am fairly new to R so apologies if this is a stupid posting.

Hi there, I have a major problem (major for me that is) with solve.QP and I'm new at this. You see, to solve my quadratic program I need to have the lagrange multipliers after each iteration. Solve.QP gives me the solution, the unconstrained solution aswell as the optimal value. Does anybody have an idea for how I could extract the multipliers? Thanx, Serge "Beatus qui prodest quibus

I am new to using R and would appreciate some advice on which books to start with to get up to speed on using R. My Background: 1-C# programmer. 2-Programmed directly using IMSL (Now Visual Numerics). 3- Used in past SPSS and Statistica. I put together a list but would like to pick the "best of" and avoid redundancy. Any suggestions on these books would be helpful (i.e. too much

Hi all, Please recommend good books for the following three categories. (I am aim at finance, macroeconomics, trading and business applications). (1) statistical (financial) data analysis; (2) time series; (3) econometrics. More specifically, I am looking for the following two types of books: (1) Books that provide big pictures and intuitions and books that connect dots... For example, there

Hi! I know that there is function in fBasics package for univariate Jarque-Bera test and a funtion for univariate Ljung-Box test in stats package. But I am wondering if there is a function somewhere to do the tests for multivariate time series? Thanks, John [[alternative HTML version deleted]]

Hi there, I'm in desperate need to figure out how to solve this issue. I need to estimate a recursive model for a time series data of asset returns. The dependent variable is the asset return and then I have a set of k variables, a lagged value of the dependent variable (plus an intercept) as regressors. My sample period (monthly observations) starts on Jan 1972. What I need to do is the

Hi, I would like to use a weighted lm model to reduce heteroscendasticity. I am wondering if the only way to generate the weights in R is through the laborious process of trial and error by hand. Does anyone know if R has a function that would automatically generate the weights need for lm? Thanks, -- Tom [[alternative HTML version deleted]]

Are there implementations of, e.g. Neville's algorithm, for interpolating polynomials through some data points? Nevilles' is an improvement on Lagrange interpolation. And how about interpolating rational functions? I could not find anything at rseek.org or at crantastic.org. thanks -- View this message in context:

Dear all, Does anybody know whether the (general) Lagrange Multiplier testing framework for restrictions on linear models has been implemented in some package? My goal is to test for omitted variables, i.e. restrictions of the kind beta_i=0, in the specification of an econometric model. There are some particular implementations in this fashion in the lmtest package (e,g, the bgtest() function,

Hi, I am using solve.QP (from quadprog) to solve a standard quadratic programming problem: min_w -0.5*w'Qw st ... I would like solve.QP to do two things: 1) to start the optimization from a user-supplied initial condition; i.e., from a vector w_0 that satisfies the constraints, and 2) to return the values of the lagrange multiplieres associated with the constraints. I did not find an obvious

I'm curious to find out if there is an example of R code for optimization of two variable function, with contraints, using lagrange multiplier (using optim/nlm?). I have a problem that contains one discrete variable, but need a simple problem/example to start with. I haven't been able to find any examples and thought I should ask here before I plunged into writing a few miles of R code.

Dear all I am working on unbalanced panel data and I can readily fit a "pooling" model using plm(), but not a "within" or "random" model. Reproducing the examples in vignette("plm") and in the AER package I encountered no such issues. ##unfortunately I cannot disclose the data, and it is too big anyway > dim(ibes.kld.exp.p[x.subs , ]) [1] 13189 34

Dear All, I couple of weeks ago, I’ve asked for a package recommendation for nonlinear optimization. In my problem I have a fairly complicated non-linear objective function subject to one non-linear equality constrain. I’ve been suggested to use the *Rdonlp2* package, but I did not get any results after running the program for 5 hrs. Is it normal to run this type of programs for hours? Also,

Hello, All. We have Samba 2.0.7 running on Red Hat 6.2 (up for 351 days!) and have discovered the following anomaly: There is a share called "appsg" that contains a number of folders, including one called OfficeTemplates. The share definition in smb.conf is: [appsg] comment = Apps in Applications path=/home/applications/apps public = No read only =

Hello I''d like to know if there''s a way to minimize a non linear function in R subject to some linear restrictions Ax=b I have tried to use optim() but it doesn''t seem to be able to do it. Or, by Lagrange multipliers, solve some non linear equations simultaneously. Anyone can help? Thanks -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Hello, First, sorry for my poor english, I will try to be understood. It's the first time I try this "r-help mailing list" and I hope it will be a success. I am working on heteroscedasticity analysis. I would like to get the "Box-Ljung" and the "Lagrange multipliers" test. I found the first one in the library "ts", but I can't find the second one.

Dear all, I have two functions (f1, f2) and 4 unknown parameters (p1, p2, p3, p4). Both f1 and f2 are functions of p1, p2, and p3, denoted by f1(p1, p2, p3) and f2(p1,p2,p3) respectively. The goal is to maximize f1(p1, p2, p3) subject to two constraints: (1) c = k1*p4/(k1*p4+(1-k1)*f1(p1,p2,p3)), where c and k1 are some known constants (2) p4 = f2(p1, p2, p3) In addition, each parameter