Sales tax data is a continuing theme for me these days and so I thought we could look at any seasonal patterns in the data. I am going to hold off on the formal statistical tests for right now and we will go with the graphical approach. If there is seasonality in the data the graph should show common movements in the lines for different years. For example, if June is always a slow month for retail sales the collections should drop for most June observations compared to the May observations. I generate this for 2001 to 2017.
A closer look at recent sales tax data seems a logical follow-up to the long run view from last time. There are two things to note from this graph: 1) the negative trend is still clearly evident, and 2) the reduction in volatility from the twelve month rolling is clear.
The January sales tax collections data came out recently and I want to take a longer view of the data here. This typically means messier graphs because the time scale gets compressed. I will put up a post with a shorter timeline tomorrow. Why the longer timeline? I think it is necessary if we are going to try and interpret or understand the numbers we are currently seeing.
It was a busy week with a visit by the Minneapolis Federal Reserve President, Neel Kashkari. I will post about that later probably. With the other economic news coming out about the shelving of another vote on healthcare overhaul and the release of a tax plan, wages and income seem to be as relevant now as they were in the last few weeks.
JT and I are devoting a portion of my weekly appearance to the Grand Forks Flood of 1997, and the business and economic consequences of the event. The consequences of some events are best understood with the perspective of time, and natural disasters are clearly this type of event. For today’s post I chose to look at unemployment, but in a slightly different way.