Read Before You Cite!

Note to self - file this post in the "Look Before You Leap" category!

Looking at The New Zealand Herald newspaper this morning, this headline caught my eye:

"How Did Sir Owen Glenn's Domestic Violence Inquiry Get $7 Billion Figure Wrong?"

$7 Billion? Even though that's (only) New Zealand dollars, it still sounds like a reasonable question to ask, I thought. And (seriously) this is a really important issue, so, I read on.

Here's part of what I found (I've added the red highlighting):

Price Indices Based on Scanner Data

Among the many interesting presentations that I attended at the recent Annual Conference of the N.Z. Association of Economists was one by Frances Krsinich, from Statistics New Zealand.

The paper that Frances gave was titled "Price Indexes From Online Data Using the Fixed Effects Window Splice (FEWS) Method". Here's the abstract:

Unit Root Testing: Sample Size vs. Sample Span

The more the merrier when it comes to the number of observations we have for our economic time-series data - right? Well, not necessarily. 

There are several reasons to be cautious, not the least of which include the possibility of structural breaks or regime-switching in the data-generating process. However, these are topics for future posts. Here, I want to discuss a different issue - namely, the impact of data frequency on the properties of tests for the stationarity of economic time-series.

To be specific, let's consider the following question: "Which is better when I'm applying the (augmented) Dickey-Fuller test - 20 annual observations for the series, or 80 quarterly observations?"

"Disappearing" Historical Data

It's the bane of my life - and probably that of every other economist who (tries to) work(s) with time-series data. Historical data that simply disappear.

Shazam! Now you see, it; now you don't!

You know the sort of thing I mean, I'm sure. Just when you think you have a nice long, consistent, series of economic data, the statistical agency in question suddenly stops recording it. They change the basis on which the data are gathered (usually for perfectly good reasons), and leave you with a big red DISCONTINUED descriptor.

Gee - thanks a lot!

Sometimes they even "pull" the historical series that you were really counting on, and just provide you with a pretty new series that started yesterday, and won't be of any use to anyone for ages.