2043 search results for "regression"

Some R Resources for GLMs

April 3, 2014
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Some R Resources for GLMs

by Joseph Rickert Generalized Linear Models have become part of the fabric of modern statistics, and logistic regression, at least, is a “go to” tool for data scientists building classification applications. The ready availability of good GLM software and the interpretability of the results logistic regression makes it a good baseline classifier. Moreover, Paul Komarek argues that, with a...

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Inference for ARCH processes

April 2, 2014
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Inference for ARCH processes

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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IV Estimates via GMM with Clustering in R

April 1, 2014
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IV Estimates via GMM with Clustering in R

In econometrics, generalized method of moments (GMM) is one estimation methodology that can be used to calculate instrumental variable (IV) estimates. Performing this calculation in R, for a linear IV model, is trivial. One simply uses the gmm() function in the excellent gmm package like an lm() or ivreg() function. The gmm() function will estimate

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Process and observation uncertainty explained with R

March 31, 2014
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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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Bayesian Data Analysis [BDA3 – part #2]

March 30, 2014
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Bayesian Data Analysis [BDA3 – part #2]

Here is the second part of my review of Gelman et al.’ Bayesian Data Analysis (third edition): “When an iterative simulation algorithm is “tuned” (…) the iterations will not in general converge to the target distribution.” (p.297) Part III covers advanced computation, obviously including MCMC but also model approximations like variational Bayes and expectation propagation

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Theil’s Blus Residuals and R Tools for Testing and Removing Autocorrelation and Heteroscedasticity

March 28, 2014
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Guest post by Hrishikesh (Rick) D. Vinod, Professor of Economics, Fordham University. Theil (1968) proposed a transformation of regression residuals so that they are best, unbiased, linear, scalar (BLUS). No R code is available to implement them. I am providing the detailed description of the properties of BLUS residuals to the uninitiated and code. The matrix algebra itself is...

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Why use R? Five reasons.

March 27, 2014
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Why use R? Five reasons.

In this post I will go through 5 reasons: zero cost, crazy popularity, awesome power, dazzling flexibility, and mind-blowing support. I believe R is the best statistical programming language to learn. As a blogger who has contributed over 150 posts in Stata and over 100 in R I have extensive experience with both a proprietary statistical programming language...

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Bayesian Data Analysis [BDA3]

March 27, 2014
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Bayesian Data Analysis [BDA3]

Andrew Gelman and his coauthors, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin, have now published the latest edition of their book Bayesian Data Analysis. David and Aki are newcomers to the authors’ list, with an extended section on non-linear and non-parametric models. I have been asked by Sam Behseta to write

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R User Group Activity for Q1 2014

March 27, 2014
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R User Group Activity for Q1 2014

by Joseph Rickert Worldwide R user group activity for the first Quarter of 2014 appears to be way up compared to previous years as the following plot shows. The plot was built by counting the meetings on Revolution Analytics R Community Calendar. R users continue to value the live, in person events and face-to-face meetings with their peers. Moreover,...

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MCMC for Econometrics Students – Part IV

March 26, 2014
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MCMC for Econometrics Students – Part IV

This is the fourth in a sequence of posts designed to introduce econometrics students to the use of Markov Chain Monte Carlo (MCMC, or MC2) simulation methods for Bayesian inference. The first three posts can be found here, here, and here, and I'll assume that you've read them already. The emphasis throughout is on the...

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