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Tuesday, August 6, 2013. Statsmodels Release 0.5.0rc1. After approximately a year since our last release, we are finally ready again for a new release of statsmodels. Skipper pushed the distribution files to pypi. During last year we merged many additional new features, and continued to improve our traditional models,. For now, I just copied, and lightly edited, some of the release information from the documentation. Statsmodels 0.5 is a large and very exciting release that brings together a year of ...

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Tuesday, August 6, 2013. Statsmodels Release 0.5.0rc1. After approximately a year since our last release, we are finally ready again for a new release of statsmodels. Skipper pushed the distribution files to pypi. During last year we merged many additional new features, and continued to improve our traditional models,. For now, I just copied, and lightly edited, some of the release information from the documentation. Statsmodels 0.5 is a large and very exciting release that brings together a year of ...

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joepy | jpktd.blogspot.com Reviews

https://jpktd.blogspot.com

Tuesday, August 6, 2013. Statsmodels Release 0.5.0rc1. After approximately a year since our last release, we are finally ready again for a new release of statsmodels. Skipper pushed the distribution files to pypi. During last year we merged many additional new features, and continued to improve our traditional models,. For now, I just copied, and lightly edited, some of the release information from the documentation. Statsmodels 0.5 is a large and very exciting release that brings together a year of ...

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jpktd.blogspot.com

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joepy: March 2012

http://jpktd.blogspot.com/2012_03_01_archive.html

Sunday, March 4, 2012. Numerical Accuracy in Linear Least Squares and Rescaling. Warning upfront: there are problems to replicate this, more below). A week ago, I stumbled on this Numerical Comparison of Statistical Software. Which presents some test results for numerical accuracy of statistical packages. The model is a polynomial of degree 10. Description, data, certified values and a plot are on the NIST website here. I parsed the data into an array. First column is the endogeous,. Variable. I saved.

2

joepy: May 2012

http://jpktd.blogspot.com/2012_05_01_archive.html

Thursday, May 10, 2012. Regression Plots - Part 1. I started to work on improving the documentation for the regressions plot in statsmodels. (However, I realized I have to improve them a bit.). For now, just a question: Can you spot the mis-specification of the model? I simulate a model, run a linear regression on three variables and a constant. Here is the estimation summary, which looks quite good, large R-squared, all variables significant, no obvious problems:. Is an OLS results instance. Where we st...

3

joepy: Quasi-Random Numbers with Halton Sequences

http://jpktd.blogspot.com/2013/06/quasi-random-numbers-with-halton.html

Tuesday, June 18, 2013. Quasi-Random Numbers with Halton Sequences. Just two quick plots. For maximum simulated likelihood estimation and for some other cases, we need to integrate the likelihood function with respect to a distribution that reflects unobserved heterogeneity. When numeric integration is too difficult, then we can integrate by simulating the underlying distribution. The first figure contrasts the Halton sequences and draws from a random number generator, on the left side for the uniform an...

4

joepy: February 2012

http://jpktd.blogspot.com/2012_02_01_archive.html

Sunday, February 12, 2012. Using rst2blogger and Breush-Godfrey test. This is mainly a test to see if rst2blogger. Works The description sounded like just what I needed to make going from rst file to blogger less work. Some setup inconveniences: Why do python programs need to install distribute and destroy my setuptools? Reminds me of the sound I'm hearing when the kids are just guessing in a numbers game: "Try again", "Try again", .). T test for linear restrictions. F test for linear restrictions. Build...

5

joepy: October 2012

http://jpktd.blogspot.com/2012_10_01_archive.html

Wednesday, October 17, 2012. TOST: statistically significant difference and equivalence. Or "Look I found a dime". To have an example we can simulate two samples, and let's take as a true difference a dime, 0.1. Payoff s1 = sigma * np.random.randn(nobs) payoff s2 = 0.1 sigma * np.random.randn(nobs). I picked sigma=0.5 to get good numbers for the story. Two Tests: t-test and TOST. We compare two test, a standard t test for independent samples and a test for equivalence, two one-sided tests, TOST:. As the ...

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joepy

Tuesday, August 6, 2013. Statsmodels Release 0.5.0rc1. After approximately a year since our last release, we are finally ready again for a new release of statsmodels. Skipper pushed the distribution files to pypi. During last year we merged many additional new features, and continued to improve our traditional models,. For now, I just copied, and lightly edited, some of the release information from the documentation. Statsmodels 0.5 is a large and very exciting release that brings together a year of ...

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