# 966 search results for "latex"

## Stationarity of ARCH processes

April 6, 2014
By
$\phi$

In the context of AR(1) processes, we spent some time to explain what happens when  is close to 1. if  the process is stationary, if  the process is a random walk if  the process will explode Again, random walks are extremely interesting processes, with puzzling properties. For instance, as , and the process will cross the x-axis an infinite number...

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## Choose Your Own Data Adventure

April 5, 2014
By

The question is: can we automate scientific discovery, and what might an interface to such a tool look like. I’ve been experimenting with automating simple and complex data analysis and report generation tasks for biological data and mostly using R and LATEX. You can see some of my progress and challenges encountered in the presentation

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## Regressions with Multiple Fixed Effects – Comparing Stata and R

April 5, 2014
By

In my paper on the impact of the recent fracking boom on local economic outcomes, I am estimating models with multiple fixed effects. These fixed effects are useful, because they take out, e.g. industry specific heterogeneity at the county level - or state specific time shocks. The models can take the form:    where is

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## Two R tutorials for beginners

Two R tutorials for beginnersI am currently in the process of rescuing some of the pages from my now defunct datajujitsu.co.uk blogger blog and moving to this Github/Clojure/Bootstrap version. I also today gave a tutorial to the University of Manche...

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## Inference for ARCH processes

April 2, 2014
By
$p$

Consider some ARCH() process, say ARCH(), where with a Gaussian (strong) white noise . > n=500 > a1=0.8 > a2=0.0 > w= 0.2 > set.seed(1) > eta=rnorm(n) > epsilon=rnorm(n) > sigma2=rep(w,n) > for(t in 3:n){ + sigma2=w+a1*epsilon^2+a2*epsilon^2 + epsilon=eta*sqrt(sigma2) + } > par(mfrow=c(1,1)) > plot(epsilon,type="l",ylim=c(min(epsilon)-.5,max(epsilon))) > lines(min(epsilon)-1+sqrt(sigma2),col="red") (the red line is the conditional variance process). > par(mfrow=c(1,2)) > acf(epsilon,lag=50,lwd=2)...

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## Modeling the Marginals and the Dependence separately

April 1, 2014
By
$F$

When introducing copulas, it is commonly admitted that copulas are interesting because they allow to model the marginals and the dependence structure separately. The motivation is probably Sklar’s theorem, which says that given some marginal cumulative distribution functions (say  and , in dimension 2), and a copula (denoted ), then we can generate a multivariate cumulative distribution function with...

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## Include uncertainty in a financial model

April 1, 2014
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Here’s a post that appears on my new website, ragscripts.com. On-line resources for analysts are often either too general to be of practical use or too specialised to be accessible. The aim of ragscripts.com is to remedy this by providing start to finish directions for complex analytical tasks. The site is under construction at the … Continue reading...

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## Correlation with constraints on pairs

March 31, 2014
By
$\text{cov}(X,Y)$

An interesting question was posted on http://math.stackexchange.com/726205/…: if one knows the covariances  and , is it possible to infer ? I asked myself a question close to this one a few weeks ago (that I might also relate to a question I asked a long time ago, about possible correlations between three exchange rates, on financial markets). More precisely, if one knows the...

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## Process and observation uncertainty explained with R

March 31, 2014
By
$Process and observation uncertainty explained with R$

Once up on a time I had grand ambitions of writing blog posts outlining all of the examples in the Ecological Detective.1 A few years ago I participated in a graduate seminar series where we went through many of the examples in this book. I am not a population biologist by trade but many of

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## Moustache target distribution and Wes Anderson

March 31, 2014
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$Moustache target distribution and Wes Anderson$

Today I am going to introduce the moustache target distribution (moustarget distribution for brievety). Load some packages first. Let’s invoke the moustarget distribution. This defines a target distribution represented by a SVG file using RShapeTarget. The target probability density function is defined on and is proportional to on the segments described in the SVG files,

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