similar to: looking for functions that can test/estimate CAMPM, APT, Fama's factor model, etc.

Hi all, I was wondering whether there is a standard method to carry out fama-macbeth regressions in R. I have spent the last few hours looking around the help pages but nothing seems to be written about this. Thanks a lot! [[alternative HTML version deleted]]

All, I'm attempting to run this: table.CAPM(series[,"Strat.Return",drop=FALSE],series[,"spy.Return",drop=FALSE]) and getting this error Error in as.vector(data[, i]) : subscript out of bounds I've searched around and cannot find a solution to the problem.  I've used this in the past without problem and I'm not sure what I did different this time. Other

Hi This is my function. It serves an HTML page after the calculations. I'm connecting to a MSSQL DB using pyodbc. def CAPM(self,client): r=self.r cds="1590" bm="20559" d1 = [] v1 = [] v2 = [] print"Parsing GET Params" params=client.g[1].split("&") for items in

Hey Guys, i want to do a CAPM-GARCH model. I didn?t find anything posted online. (If there is something - shame on me - i didn?t find it.) My Problem: What is the difference if I let the residuals ?e? follow a garch process ? How do I do my regression analysis now? I began reading about regression analyis with heteroscedasticity, but didn?t get it. So i started programming. First

Hi, I have a problem with a regression I try to run. I did an estimation of the market model with daily data. You can see to output below: /> summary(regression_resn) Time series regression with "ts" data: Start = -150, End = -26 Call: dynlm(formula = ror_resn ~ ror_spi_resn) Residuals: Min 1Q Median 3Q Max -0.0255690 -0.0030378 0.0002787

Hi all ARCH modelling I have a problem now on how to proceed with further steps in my analysis. I did a linear OLS regression with my daily data of stock and index returns. There is now the problem of arch in my error terms. Thus I used the following r command: garch(resid_desn, order=c(0,2)) ## This ARCH(2) process seems to fit the best after trial and error. Consequently, I get there three

Hi mates, I have a problem. I am new to R and want to conduct the Fama/French asset pricing test. As I am from Germany, I cannot use the already computed factors from French's website, but need to compute them myself. So I have to sort a number of stocks into different portfolios using one factor, then subdivide these portfolios into several more using another factor, then compute portfolio

Hola a tod en s, tengo una duda que se relaciona con alternativas al uso de variables globales. En principio, si se quiere usar un generador de v.a con la librería Runuran sólo se permite definir las funciones de densidad (o el núcleo de las mismas) con funciones con un único argumento en (x). Sin embargo, necesito pasar a las funciones más argumentos que van cambiando en las iteraciones de

On 30/08/14 23:02, Ilia Mirkin wrote: > Samplers are only defined up to num_samplers, so set all samplers above > nr to NULL so that we don't try to read them again later. > Would it be worth doing a similar thing with the unlocked samplers below the nr mark ? It seems to me that we might be leaking nv50->samplers[s][i], or perhaps I'm missing something ? -Emil >

Dear List, I've just tried to specify a GAM without an intercept -- I've got one of the (rare) cases where it is appropriate for E(y) -> 0 as X ->0. Naively running a GAM with the "-1" appended to the formula and the calling "predict.gam", I see that the model isn't behaving as expected. I don't understand why this would be. Google turns up this old

Dear R-list, I?m trying to use the lmer of the lme4 package to fit a linear mixed model of the form Y = Xb + Zu + e and I can?t figure out how to control the covariance structure of u. I want u ~ N(0,sigma^2*I). More precisely I?m trying to smooth a curve through data using the "Penalized Splines as BLUPs" method as described in Ruppert, Wand & Carroll (2003). So I have Z = [Z1

Hi everybody, I am currently working on mixtures of two densities ( f(xi,teta)= (1-teta)*f1(xi) + teta*f2(xi) ), particularly on the behavior of the variance for teta=0 (so sample only comes from the first distribution). To determine the maximum likelihood estimator I use the Newton-Rapdon Iteration. But when computing the first derivative I get a none linear function (with several asymptotes)

Dear all, I want to do piecewise CAPM linear regression in R: RRiskArb−Rf = (1−δ)[αMktLow+βMktLow(RMkt−Rf)] + δ[αMkt High +βMkt High(RMkt −Rf )] where δ is a dummy variable if the excess return on the value-weighted CRSP index is above a threshold level and zero otherwise. and at the same time add the restriction: αMkt Low + βMkt Low · Threshold = αMkt High + βMkt High · Threshold to ensure

Inthe code below I was trying to to obtain the GMM estimates for CAPM (REGRESSION) for 36 stocks each have 180 observations,however it only gives me one output rather than 36. In SAS i would just put in a *By statement*. I have a variable TICKER that categorize them into 36 groups. *How can I obtain all 36 output instead of just one.* **

Hi I am just starting to look at R and trading in futures, stock, etc Can anyone point me to useful background material?

In certain circumstances, findFirstUses could end up doubling back on instructions it had already processed, resulting in an infinite recursion. Avoid this by keeping track of already-visited instructions. Bugzilla: https://bugs.freedesktop.org/show_bug.cgi?id=83079 Tested-by: Tobias Klausmann <tobias.johannes.klausmann at mni.thm.de> Signed-off-by: Ilia Mirkin <imirkin at

Dear R users, I'm trying to specify a generalized linear mixed model in R, basically a Poisson model to describe monthly series of counts in different regions. My aim is to fit subject-specific curves, modelling a non-linear trend for each region through random effects for linear splines components (see Durban et al, Stat Med 2005, or " Semiparametric regression" by Ruppert et al,

Hi all I am looking for an add-in. I am currently working on something and I use daily data of closing stock prices. As not all companies are traded daily (e.g. on monday, then on thursday etc) at the stock exchange, there is satistically a problem. There are some papers which explain the approach to handle infrequent trading of a stock or non synchronous data and beta estimation (Dimson, 1979;

This isn't all that likely to be homework, Bert.... However, Alexander, you may find that not many readers are familiar with YTM concepts. There's a chapter with R examples in Ruppert+Matteson's book (if you have SpringerLink, you may be able to download for free). Otherwise you could try searching CRAN, but be warned that you may get considerably more than you wished for. Some

Hello, I am a bit confused about gamm in mgcv. Consulting Wood (2006) or Ruppert et al. (2003) hasn't taken away my confusion. In this code from the gamm help file: b2<-gamm(y~s(x0)+s(x1)+s(x2)+s(x3),family=poisson,random=list(fac=~1)) Am I correct in assuming that we have a random intercept here....but that the amount of smoothing is also changing per level of the