Personal Consumption Expenditures: Dec 2013 Preview

Tomorrow’s report on personal consumption spending for December is projected to show a gain of 0.3% vs. the previous month, based on The Capital Spectator’s median econometric forecast. That’s below the previously released 0.5% increase for November. Meanwhile, the Capital Spectator’s median forecast for December is slightly above three consensus predictions 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:

pce.30jan2014

VAR-1: A vector autoregression model that analyzes the history of personal income in context with personal consumption expenditures. The forecasts are run in R with the “vars” package.

VAR-3: A vector autoregression model that analyzes three economic time series in context with personal consumption expenditures. The three additional series: US private payrolls, personal income, and industrial production. The forecasts are run in R with the “vars” package.

ARIMA: An autoregressive integrated moving average model that analyzes the historical record of personal consumption expenditures in R via the “forecast” package to project future values.

ES: An exponential smoothing model that analyzes the historical record of personal consumption expenditures in R via the “forecast” package to project future values.

R-1: A linear regression model that analyzes the historical record of personal consumption expenditures in context with retail sales. The historical relationship between the variables is applied to the more recently updated retail sales data to project personal consumption expenditures. The computations are run in R.

TRI: A model that’s based on combining forecasts with a technique known as triangular distributions. The forecast combinations include the following projections: Econoday.com’s consensus forecast data and the six predictions generated by the models noted above, i.e., VAR-1, VAR-3, ARIMA, ES, and R-1. The forecasts are run in R with the “triangle” package. For more information about TRI, see this post.

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