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Alternative Seasonal Adjustments Confirm Strong Jobs Growth

Jonathan Wright
Jonathan Wright Professor - Johns Hopkins University

January 9, 2015

There are massive seasonal patterns in employment data. For example, in July, it is typical for the US economy to lose over a million jobs. Adjusting for this normal seasonal variation is essential to interpreting month-to-month changes in employment. The approach for this seasonal adjustment that is presently used by the Bureau of Labor Statistics (BLS) puts very heavy weight on the current and last two years of data in assessing what are the typical patterns for each month.

Jonathan Wright of Johns Hopkins University argues in “Unseasonal Seasonals?” that a longer window should be used to estimate seasonal effects.  He finds that using a different seasonal filter, known as the 3×9 filter, produces better results and more accurate forecasts. The key difference in the 3×9 filter is that it spreads weight over the most recent six years in estimating seasonal patterns. This makes the seasonal patterns more stable over time than in the current BLS seasonal adjustment method.

We calculate the month-over-month change in total nonfarm payrolls, seasonally adjusted by the 3×9 filter, for the most recent month. The corresponding data as published by the BLS are shown for comparison purposes. According to the alternative seasonal adjustments, the economy gained 244 thousand jobs last month. The Bureau of Labor Statistics reported that the economy gained 252 thousand jobs last month. The discrepancies between the two series are explained in Dr. Wright’s BPEA paper.

 

 

Thousands of Jobs Added

BLS

Wright

2014-December

252

244

 

2014-November

353

388

 

2014-September

261

261

 

2014-August

271

270

 

2014-July

203

208

 

2014-June

243

230

 

2014-May

267

259

 

2014-April

229

224

 

2014-March

304

306

 

2014-February

203

204

 

2014-January

222

241

 

2013-December

144

137