Leading economic indicators have long been a tool of American economists, particularly\r\nthose working in the business sector, for anticipating turning points in the business cycle.\r\nArmed with knowledge of likely peaks and troughs in the pace of aggregate economic\r\nactivity, business economists can advise corporate leaders as to the probable path of the\r\nmacroeconomy, thereby influencing if not improving the quality of strategic decision making\r\nwithin organizations. This chain of events is predicated on the assumed reliability of leading\r\nindicators to forecast correctly the future, an assumption put to the test in this paper via a\r\nnovel application of statistical process control (SPC) to a well-known set of leading\r\nindicators that have been studied for the better part of half a century.\r\nTo give context to the overall discussion, the paper begins with a quick review of the\r\nhistorical development of leading indicator forecasting as it evolved in the United States. This\r\nis followed with an explanation of statistical process control, the singular methodology used\r\nin this paper, but one seldom employed in general economic analysis save for the area of\r\nproduction economics and its emphasis on manufacturing. Once explained, the SPC process\r\nis applied to a representative set of eleven leading indicators that have been tracked quarterly\r\nor more frequently for anywhere from 38 to 71 years.\r\nThe results of the SPC analysis of this data pool of some 7,000+ observations suggest that\r\ncollectively leading indicators reliably forecast business-cycle turning points, with the caveat\r\nthat individually the effectiveness with which specific indicators within a set predict the\r\nfuture of the macroeconomy is subject to wide variation.
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