This article presents the Trader Edge aggregate neural network model forecast for the January 2015 non-farm payroll data, which is scheduled to be released tomorrow morning at 8:30 AM EST.
Non-Farm Payroll (NFP) Model Forecast - January 2015
The Trader Edge aggregate NFP model represents the average of three neural network forecasting models, each of which employs a different neural network architecture. Unlike expert systems, neural networks use algorithms to identify and quantify complex relationships between variables based on historical data. All three models derive their forecasts from seven explanatory variables and the changes in those variables over time.
The table in Figure 1 below includes the monthly non-farm payroll data for two months: December 2014 and January 2015. The December data was released last month and the non-farm payroll data for January 2015 will be released tomorrow morning at 8:30 AM EST.
The model forecasts are in the third data row of the table (in blue). Note that past and current forecasts reflect the latest values of the independent variables, which means that forecasts will change when revisions are made to the historical economic data.
The monthly standard error of the model is approximately 82,200 jobs. The first and last data rows of the table report the forecast plus 0.5 standard errors (in green) and the forecast minus 0.5 standard errors (in red), respectively. All values are rounded to the nearest thousand. If the model errors were normally distributed, roughly 31% of the observations would fall below -0.5 standard errors and another 31% of the observations would exceed +0.5 standard errors.
The actual non-farm payroll release for December 2014 is in the second data row of the table (in purple). The consensus estimate (reported by Briefing.com) for January 2015 is also in the second data row of the table (in purple). The reported and consensus NFP values also include the deviation from the forecast NFP (as a multiple of the standard error of the estimate). Finally, the last column of the table includes the estimated changes from December 2014 to January 2015.
The aggregate neural network model forecast for January is 251,000, which is up a slight 9,000 jobs from last month's revised forecast of 242,000. The small increase in the forecast from December to January reflects a minor strengthening in the employment environment during the month of January. The Briefing.com consensus estimate for January is 235,000, which is down 17,000 jobs from the December report, indicating a softening in the employment environment. The actual December data was slightly above the revised December forecast (+0.12 S.E.) and the consensus estimate for January is slightly lower than the model forecast (-0.19 S.E.).
If we ignore the outlier in the December 2013 data (-2.03 S.E), there has been a gradual and sustained positive trend in the employment data from mid-2012 through today. The trend is easier to see in the forecast data due to fewer outliers. To test the strength of the trend that began in mid-2012, I ran a simple linear regression on the monthly NFP forecast data (dependent variable) against time (independent variable).
The R-squared of the linear regression was 56%, indicating that 56% of the variation in the forecast data was explained by the simple linear trend model. The coefficient for the monthly slope variable was 3.12, which represented an increase of 3,100 jobs per month. The slope coefficient was highly significant with a t-statistic of 6.41 and a P-value of 3.31E-07. The standard error of the model was 27,850 jobs.
The simple linear regression model cannot be used long-term due to the cyclical nature of employment, but it can be useful for quantifying short-term trends in the data. If you are curious, the linear regression model is predicting a gain of 265,000 jobs in January, which is very close to both the aggregate model forecast and the Briefing consensus estimate.
I repeated the regression exercise with the actual NFP data and the results were similar, but the explanatory power of the model was lower due to the large outliers in the actual NFP data. Not surprisingly, the variation in the actual NFP reports is much wider than the variation in the NFP forecasts. Unfortunately, the Government reports are notoriously noisy, so the trend is more apparent in the forecast data, which are based on several different economic variables that collectively give a much more accurate and reliable employment reading than the Government data.
The small difference between the January NFP consensus and January NFP forecast suggests a very small probability of an upside surprise tomorrow. The actual December 2013 NFP data was clearly an outlier, but the Trader Edge model has tracked the actual NFP data very closely over the past few years.
Basic forecasting tools can help you identify unusual consensus economic estimates, which often lead to substantial surprises and market movements. Identifying such environments in advance may help you protect your portfolio from these corrections and help you determine the optimal entry and exit points for your strategies.
In the case of the NFP data, the monthly report data is highly variable and prone to substantial revisions. As a result, having an independent and unbiased indicator of the health of the U.S. job market is especially important.
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