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Tomorrow's US industrial production report for September will be postponed due to the government shutdown. Although the numbers are produced by the Federal Reserve, which remains operational thanks to funding that's independent of Congressional approval, the central bank advises that this report relies on "a range of data from other government agencies, the publication of which has been delayed as a result of the federal government shutdown." When (or if) September's report sees the light of day is unclear at this point. That said, industrial production for September is expected to rise 0.2% over the previous month, according to The Capital Spectator's average econometric forecast. Keep in mind that this forecast is impaired because it doesn't reflect updates to the R-4 and VAR-7 models (see definitions below), which rely in part on the latest payrolls data. 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 0.2% rise for September industrial production represents a lesser pace from the previously reported 0.4% gain for August. Meanwhile, the Capital Spectator's average projection for September is below 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:

R-1: A linear regression model using the ISM Manufacturing Index to predict industrial production. The historical relationship between the variables is applied to the more recently updated ISM data to project industrial production. The computations are run in R.

R-4: A linear regression model using four variables to project industrial production: US private payrolls, an index of weekly hours worked for production/nonsupervisory employees in private industries, the ISM Manufacturing Index, and the stock market (S&P 500). The historical relationships between the variables are applied to the more recently updated data to project industrial production. The computations are run in R.

VAR-1: A vector autoregression model using the ISM Manufacturing Index to predict industrial production. VAR analyzes the interdependent relationships of the variables through history. The forecasts are run in R using the "vars" package.

VAR-7: A vector autoregression model using seven variables to project industrial production: US private payrolls, an index of weekly hours worked for production/ nonsupervisory employees in private industries, the ISM Manufacturing Index, the stock market (S&P 500), real personal income less current transfer receipts, real personal consumption expenditures, and oil prices. VAR analyzes the interdependent relationships through history. The forecasts are run in R using the "vars" package.

ARIMA: An autoregressive integrated moving average model that analyzes the historical record of industrial production in R via the "forecast" package to project future values of the data set.

ES: An exponential smoothing model that analyzes the historical record of industrial production in R via the "forecast" package to project future values of the data set.

Source: U.S. Industrial Production: September 2013 Preview