{"id":6674393,"date":"2020-07-02T21:51:42","date_gmt":"2020-07-02T21:51:42","guid":{"rendered":"http:\/\/cfe.econ.jhu.edu\/?p=6674393"},"modified":"2022-05-17T13:01:55","modified_gmt":"2022-05-17T13:01:55","slug":"inside-baseball","status":"publish","type":"post","link":"https:\/\/krieger.jhu.edu\/financial-economics\/2020\/07\/02\/inside-baseball\/","title":{"rendered":"Inside Baseball"},"content":{"rendered":"\n
Seasonal adjustment is one of the more obscure potential casualties of the pandemic recession. A well known problem with seasonal adjustment is that an outlier observation will lead the seasonal adjustment process to shift its belief about what is normal for that month. In cases like the coronavirus recession, this is undesirable because the unusual shocks for the last few months don\u2019t tell us anything about what is normal for this time of year. Seasonal adjusters are of course well aware of this and have the option to manually specify that the data for a given month be treated as an outlier. That then effectively freezes the seasonal factor for that month.<\/p>\n\n\n\n
Seasonal adjustment in the BLS establishment survey is done at the disaggregate level. But we can take the difference between total not seasonally adjusted data and total seasonally adjusted data as the implicit seasonal factor in total payroll employment. The table below shows the implicit seasonal factors for April, May and June of 2019 and 2020.<\/p>\n\n\n\n