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Forecasting the recent behavior of US business fixed investment spending: an analysis of competing models This is a significantly revised version of our previous paper, 'Forecasting US Business Fixed Investment Spending'. The results reported in this paper were generated using GAUSS 6.0. The GAUSS programs are available at http:||pages.slu.edu|faculty|rapachde|Research.htm

  • Mark E. Wohar

    (Department of Economics, University of Nebraska at Omaha, Omaha, Nebraska, USA)

  • David E. Rapach

    (Department of Economics, Saint Louis University, Saint Louis, Missouri, USA)

We evaluate forecasting models of US business fixed investment spending growth over the recent 1995:1-2004:2 out-of-sample period. The forecasting models are based on the conventional Accelerator, Neoclassical, Average Q, and Cash-Flow models of investment spending, as well as real stock prices and excess stock return predictors. The real stock price model typically generates the most accurate forecasts, and forecast-encompassing tests indicate that this model contains most of the information useful for forecasting investment spending growth relative to the other models at longer horizons. In a robustness check, we also evaluate the forecasting performance of the models over two alternative out-of-sample periods: 1975:1-1984:4 and 1985:1-1994:4. A number of different models produce the most accurate forecasts over these alternative out-of-sample periods, indicating that while the real stock price model appears particularly useful for forecasting the recent behavior of investment spending growth, it may not continue to perform well in future periods. Copyright © 2007 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1010
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 26 (2007)
Issue (Month): 1 ()
Pages: 33-51

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Handle: RePEc:jof:jforec:v:26:y:2007:i:1:p:33-51
DOI: 10.1002/for.1010
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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