### Real Inflation? (Part 1)

At a recent lunch the conversation turned to how most American's real income hasn't change since the 1970s when we adjust for inflation (see here for some decent graphs). One of the people at the lunch (a person who has written considerably on monetary policy) contested this. His argument is that we are actually very bad at measuring inflation. Prices may rise, but the quality of the goods that we buy is much better now than it was in the seventies. The iPad I buy now is much better than the 1970s TV or radio or all the other things that it replaced in my life and probably cheaper than all of these things combined. On this line of reasoning, inflation is actually overestimated.

There is one obvious flaw with this argument: it misses much of the point. If we were really terrible at measuring inflation in this way, then yes maybe most peoples' income has actually increased. But the bigger issue is that the top sliver of the income distribution has made steady gains since the 1970s even using this potentially underestimated measure of inflation. If we are underestimating the gains for most people we are also underestimating the top part of the distribution's large gains as well. Reinforcing the point.

Ok, but what about this idea that we underestimate inflation because we have a difficult time correcting for improvements in the goods that people buy. Maybe there is something to this, which I'll follow up on later. . .

### Showing results from Cox Proportional Hazard Models in R with simPH

Update 2 February 2014: A new version of simPH (Version 1.0) will soon be available for download from CRAN. It allows you to plot using points, ribbons, and (new) lines. See the updated package description paper for examples. Note that the ribbons argument will no longer work as in the examples below. Please use type = 'ribbons' (or 'points' or 'lines' ). Effectively showing estimates and uncertainty from Cox Proportional Hazard (PH) models , especially for interactive and non-linear effects, can be challenging with currently available software. So, researchers often just simply display a results table. These are pretty useless for Cox PH models. It is difficult to decipher a simple linear variable’s estimated effect and basically impossible to understand time interactions, interactions between variables, and nonlinear effects without the reader further calculating quantities of interest for a variety of fitted values. So, I’ve been putting together th

### Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame. I've found the various R methods for doing this hard to remember and usually need to look at old blog posts . Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command– slide –to the DataCombine R package (v0.1.5). Building on the shift function TszKin Julian posted on his blog , slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data. Note: your data needs to be in ascending time order with equally spaced time increments. For example 1995, 1996