Time Series Modeling and the Forecasting Effectiveness of the US Leading Economic Indicators

John B. Guerard, Anureet Saxena () and Mustafa N. Gültekin
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John B. Guerard: McKinley Capital Management, LLC
Anureet Saxena: McKinley Capital Mgmt, LLC
Mustafa N. Gültekin: University of North Carolina Chapel Hill

Chapter Chapter 13 in Quantitative Corporate Finance, 2022, pp 331-401 from Springer

Abstract: Abstract An important aspect of financial decision-making may depend on the forecasting effectiveness of the composite index of leading economic indicators, LEI. The leading indicators can be used as an input to a transfer function model of real gross domestic product, GDP. The previous chapter employed four quarterly lags of the LEI series to estimate regression models of association between current rates of growth of real US GDP and the composite index of leading economic indicators. This chapter examines whether changes in forecasted economic indexes help forecast changes in real economic growth. The transfer function model forecasts are compared to several naïve models in order to test which model produces the most accurate forecast of real GDP. No-change forecasts of real GDP and random walk with drift models may be useful when forecasting benchmarks (Mincer & Zarnowitz, 1969; Granger & Newbold, 1977). Economists have constructed leading economic indicator series to serve as a business barometer of the changing US economy since the time of Wesley C. Mitchell (1913). The purpose of this study is to examine the time series forecasts of composite economic indexes produced by The Conference Board (TCB) and test the hypothesis that the leading indicators are useful as an input to a time series model to forecast real output in the United States.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-87269-4_13

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DOI: 10.1007/978-3-030-87269-4_13

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