# R Stuff

### No simulation is complete without a gif

March 24, 2011 |

I promise this is my last post on the now week and a half old π pay! Building on the last post, I figured I could show how convergence actually works in the estimation algorithm. If you’ll recall, we plotted … Continue reading → [Read more...]

### More pi plus 1 (or plus 0.01) day fun

March 15, 2011 |

Since I just didn’t get enough this morning, I spent some more time fooling around with estimating pi. Since I was basically counting the number of random x,y pairs inside a quarter circle and computing a sample average for more … Continue reading → [Read more...]

### I’m late for π day

March 15, 2011 |

It is officially no longer pi day, but I didn’t see this Drew Conway post about estimating pi until just a few minutes ago. Because Google Reader doesn’t show github embeds, I also got to try it without seeing Drew’s … Continue reading → [Read more...]

March 8, 2011 |

Two posts ago I mentioned the age-earnings profile but did not provide a regression of log earnings on wage. I also offered, without evidence, that fitting a simple linear regression would be inappropriate. How do I know that? How could … Continue reading → [Read more...]

### Our Friend the Age-Earnings Profile

March 7, 2011 |

I like Labor Economics. Partially because it has a nice mix of theory and practical empiricism, but mostly because it seems to be a sub-field with a number of agreed upon stylized facts that grow not out of micro theory … Continue reading → [Read more...]

### Bootstrapping the Truncated Normal Distribution

March 2, 2011 |

Here’s a post generated from my own ignorance of statistics (as opposed to just being marred by it)! In Labor Economics we walked through something called the truncated normal distribution. Truncated distributions come up a lot in the sciences because … Continue reading → [Read more...]

### Stationarity

February 9, 2011 |

In time series work you often run into difficulties in modeling processes where the overall level of one variable (an input, for example) changes over time but the levels of another variable (an output) do not change. For instance if … Continue reading →