Forecasting Purchasing Managers’ Index with Compressed Interest Rates and Past Values

Abstract The purchasing managers’ index (PMI) is a simple subjective survey about the state of the manufacturing sector of the national economy. It's an early indicator of the nation's economic strength with effects extending into federal monetary policy and the financial markets. It is a composite index comprising the weighted average of new orders, production, employment, supplier deliveries, and inventories. It has been established that inverted interest rates in 3-month Treasury bills is a predictor of PMI. This study extended the work on the compression of economic and financial predictor variables as well as the relative efficiency of temporal nonlinear neural network models in forecasting economic time series variables. It showed that compressed interest rates and PMI past values are also effective predictors of the future values of PMI. Less than 30% of the wavelet packets coefficients of interest rates were involved in accomplishing the forecasting task. The correlation, root mean square error, normalized root mean square error, mean absolute deviation, and Theil inequality metrics were used to determine the efficacy of the forecasts. The overall PMI forecast produced by the neural network models was relatively better than that produced by the regression models on all metrics except Theil inequality.