Chicago Fed Nat’l Activity Index: Mar 2014 Preview

The three-month average of the Chicago Fed National Activity Index (CFNAI) is expected to decline slightly to -0.26 in the March update that’s scheduled for release on Monday (April 21), according to The Capital Spectator’s median econometric forecast. In the previous release for February, the three-month average was -0.18, a reading that equates with relatively weak economic growth. Only values below -0.70 indicate an “increasing likelihood” that a recession has started, according to guidelines from the Chicago Fed. Based on today’s estimate for March, CFNAI’s three-month average is projected to remain at a level that’s historically associated with growth, but at a moderately below-trend pace.

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

cfnai.18apr2014

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.

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