Tomorrow’s report on retail sales for January is projected to show a 0.2% gain for the month, according to The Capital Spectator’s average econometric forecast. That’s slightly higher than the consensus forecasts from several surveys of economists. In all cases, the January projections are below the 0.5% gain reported by the Census Bureau last month.

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

R-2: A linear regression model that analyzes two data series in context with retail sales: an index of weekly hours worked for production/nonsupervisory employees in private industries and the stock market (S&P 500). The historical relationship between the variables is applied to the more recently updated data to project retail sales. The computations are run in R.
ARIMA: An autoregressive integrated moving average model that analyzes the historical record of retail sales in R via the “forecast” package.
ES: An exponential smoothing model that analyzes the historical record of retail sales in R via the “forecast” package.
VAR-6: A vector autoregression model that analyzes six economic time series in context with retail sales. The six additional series: US private payrolls, industrial production, index of weekly hours worked for production/nonsupervisory employees in private industries, the stock market (S&P 500), disposable personal income, and personal consumption expenditures. The forecasts are calculated in R with the “vars” package.