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One factor that is critical for any financial planning is estimating what future inflation will be. For example, if you’re saving money in an instrument that gains 3% per year, and inflation is estimated to be 4% per year, well then you’re losing money in real terms.
There are a variety of ways to estimate the rate of future inflation. You could, for example, use past rates as an estimate of future rates. However, the Treasury market provides an estimate of what the market thinks annual inflation will be over the next 5, 10, 20, and 30 years.
Basically, the Treasury issue two types of securities: nominal securities that pay a nominal interest rate (fixed percentage of your principal), and inflation-indexed securities (TIPS) that pay an interest rate that is applied to your principal adjusted by the consumer price index (CPI). As the CPI goes up and down, the payments for inflation-indexed securities go up and down (although they can’t go negative so you always get your principal back). As these securities trade throughout the day, their respective market-based interest rates go up and down continuously. The difference between the nominal interest rate and the real interest rate for a fixed period of time (5, 10, 20, years) can be used as a rough estimate of annual inflation over that time period.
The Treasury publishes data for its auctions everyday on the yield for both nominal and inflation-indexed securities. There is an XML feed for nominal yields and for real yields. Using these, I used the XML R package and wrote an R script to scrape the data and calculate the inflation estimate.
As of today, the market’s estimate of annual inflation is:
5-year Inflation: 1.88% 10-year Inflation: 2.18% 30-year Inflation: 2.38%
Basically, you just call the ‘inflation()’ function with no arguments and it produces the above print out.
In my last post I complained about efficiency not being discussed enough by NBA announcers and commentators. I pointed out that some of the best scorers have relatively low FG% or TS%. However, via the comments it was pointed out that top scorers need to take more difficult shots and thus are expected to have lower efficiency. The plot below (made with this R script) seems to confirm this (click image to enlarge) . The dashed line is from regression and the colors represent guards (green), forwards (orange) and centers (purple).
Among this group TS% does trend down with points per game and centers tend to have higher TS%. Forwards and guards are not very different. However, the plot confirms that some of the supposed all time greats are more ball hogs than good scorers.
A couple of further observations. First, Adrian Dantley was way better than I thought. Why isn’t he more famous? Second, Kobe is no Jordan. Also note Jordan played several seasons past his prime which lowered his career averages. So I added points for five of these players using only data from their prime years (ages 24-29). Here Jordan really stands out. But so does Dantley!
pd - Note that these plots say nothing about defense, rebounding, or passing. This in-depth analysis makes a convincing argument that Dennis Rodman is one of the most valuable players of all time.