US Retail Sales: September 2013 Preview

Tomorrow’s retail sales report for September will be postponed due to the government shutdown. When (if?) this update is published, US retail sales are expected to rise 0.3%, according to The Capital Spectator’s average econometric forecast. Keep in mind that this forecast is impaired because it doesn’t reflect an update of the R-2 model (see definition below), which relies in part on the latest payrolls data to project retail sales. Unfortunately, the September employment report from the government is still a mystery due to the budget impasse in Congress. Using the available numbers, the Capital Spectator’s average forecast of a 0.3% rise for September retail sales represents a slight rise from the previously reported 0.2% gain in August. Meanwhile, the Capital Spectator’s average projection for September is above several consensus forecasts based on recent surveys of economists.

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

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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 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.

This piece is cross-posted from The Capital Spectator with permission.