The objective of this article is to demonstrate the application of statistical process control (SPC) techniques to analyzing U.S. trade data including exports, imports and the overall balance of trade as supplements to existing methods. The major benefit of using these techniques would be to reduce unnecessary intervention (i.e., unnecessary corrective action) to trade policies. The basis for employing these techniques in analyzing trade data stems from an article written by Nolan and Provost (1990) in which they discussed the importance of understanding statistical variation and the importance this concept holds for managers and intent of this research is to show how SPC principles and techniques can help in an analysis of trade data so that the correct and accurate interpretation of the data will emerge and subsequently can be conveyed to decision-makers, policy-makers and other related stakeholders. The article does not intend to promote the idea that SPC techniques could or should replace the existing econometric models; it just proposes that these models (i.e, SPC) models) can be considered as supplements to the current models. The article discusses several examples of SPC applications as well is a brief description of the techniques.

Publication Date



Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type


Department, Program, or Center

Accounting (SCB)


RIT – Main Campus