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

VAR-4A: A vector autoregression model that analyzes four economic time series to project the Chicago Fed National Activity Index: the Capital Spectator’s Economic Trend & Momentum Indexes, the Philadelphia Fed US Leading Indicator, and the Philadelphia Fed US Coincident Economic Activity Indicator. VAR analyzes the interdependent relationships of these series with CFNAI through history. The forecasts are run in R with the “vars” package.

VAR-4B: A vector autoregression model that analyzes four economic time series to project the Chicago Fed National Activity Index: US private payrolls, real personal income less current transfer receipts, real personal consumption expenditures, and industrial production. VAR analyzes the interdependent relationships of these series with CFNAI through history. The forecasts are run in R with the “vars” package.

ARIMA: An autoregressive integrated moving average model that analyzes the historical record of the Chicago Fed National Activity Index in R via the “forecast” package.

ES: An exponential smoothing model that analyzes the historical record of the Chicago Fed National Activity Index in R via the “forecast” package.

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

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.