I read tea leaves which are constantly in motion. It not hard for me to paint a doom-and-gloom picture one week, and the next week say that everything is coming up roses. I do not fall in love with the words which come out of my mouth, or feel the need to produce analysis to defend many of my outlooks.Every analysis starts with a clean piece of paper.

The USA has a crappy economy since 2007 (and arguably for all of the 21st century). No data I am seeing is signaling an end to the crappy economy. My forecasts and outlook have been predominately more negative for most of 2015 – and this outlook has continued into 2016. In a big picture view, the crappy economy was expanding at an even crappier rate compared to other non-recessionary periods of the 21st century.

But economic forecasts are relative to where one is standing today. It is easy to understand that when you compare data year-over-year, a current relatively soft period may look better if you are comparing it to an even softer period.In short, all analysis is relative.

I happen to be a fan of using non-monetary data – especially data which is not subject to revision. This makes trending much more accurate as the trends are not changed when later data releases revise the previous months’ data. One of my favorite data sources is container counts from the Los Angeles ports where final data is normally issued within 15 days of period ending. This makes container counts a particularly a good forecasting tool as import final sales are added to GDP usually several months after import – while the import cost itself is subtracted from GDP in the month of import. Export final sales occur around the date of export. Container counts do not include bulk commodities such as oil or autos which are not shipped in containers. 

There has been fairly good correlation between container exports / imports and economic activity. There is no such thing as a perfect data source (or correlations) but container counts seem better than most. [Note of caution:It is dangerous to base any conclusions on a single data set].