Private nonfarm payrolls in the US are expected to increase by 173,000 (seasonally adjusted) in tomorrow’s January update from the Labor Department, according to The Capital Spectator’s median econometric point forecast. The projected rise is roughly twice as high as the previously reported increase of 87,000 for December. Meanwhile, The Capital Spectator’s median January projection is in the middle of a pair of consensus forecasts, based on surveys of economists.

Here’s a closer look at the numbers, followed by brief definitions of the methodologies behind The Capital Spectator’s projections:

ARIMA: An autoregressive integrated moving average model that analyzes the historical record of private payrolls in R via the “forecast” package.

ES: An exponential smoothing model that analyzes the historical record of private payrolls in R via the “forecast” package.

R-1: A linear regression model that analyzes the historical record of ADP private payrolls in context with the Labor Department’s estimate of US private payrolls. The historical relationship between the variables is applied to the more recently updated ADP data to project the government’s estimate of private payrolls. The computations are run in R.

VAR-6: A vector autoregression model that analyzes six economic time series in context with private payrolls. The six additional series: ISM Manufacturing Index, industrial production, aggregate weekly hours of production and nonsupervisory employees in the private sector, the stock market (S&P 500), spot oil prices, and the Treasury yield spread (10-year less 3-month T-bill). The forecasts are run in R with the “vars” package.

TRI: A model that’s based on combining point forecasts, along with the upper and lower prediction intervals (at the 95% confidence level), via a technique known as triangular distributions. The combinations include the following projections: Econoday.com’s consensus forecast data and the four predictions generated by the models noted above, i.e., ARIMA, ES, R-1, and VAR-6. The forecasts are run in R with the “triangle” package.