# Labor Market Conditions Index: August 2015 Preview

The Federal Reserve’s Labor Market Conditions Index is expected to tick lower to 1.0 in tomorrow’s update for August vs. the previous month, based on The Capital Spectator’s average point forecast for several econometric estimates. The prediction suggests that labor market conditions weakened slightly in August vs. July.

By comparison, a recent survey of economists points to a modest improvement in the Labor Market Conditions Index for August vs. the previous month.

Here’s a closer look at the numbers, followed by brief summaries of the methodologies behind the forecasts that are used to calculate The Capital Spectator’s average prediction:

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

ARIMA: An autoregressive integrated moving average model that analyzes the historical record of the Labor Market Conditions Index in R via the “forecast” package.

ES: An exponential smoothing model that analyzes the historical record of the Labor Market Conditions Index in R via the “forecast” package.

R-1: A linear regression model that analyzes the historical record of the Labor Market Conditions Index in context with the Labor Department’s estimate of US private payrolls. The historical relationship between the variables is applied to the more recently updated US payrolls data to project the Labor Market Conditions Index. The computations are run in R.

TRI: A model that’s based on combining point forecasts, along with the upper and lower prediction intervals (at the 95% confidence level), via a technique known as triangular distributions. The basic procedure: 1) run a Monte Carlo simulation on the combined forecasts and generate 1 million data points on each forecast series to estimate a triangular distribution; 2) take random samples from each of the simulated data sets and use the expected value with the highest frequency as the prediction. The forecast combinations are drawn from the following projections: Econoday.com’s consensus forecast data and the predictions generated by the models above. The forecasts are run in R with the “triangle” package.