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IT investment and Hicks' composite-good theorem: the U.S. experience

  • Jaime Marquez
  • Shing-Yi Wang

We study whether aggregation residuals in U.S. private investment in information technology (IT) exhibit a predictable pattern that is consistent with Hicks' composite-good theorem and that may be used for forecasting. To determine whether one can extract such a pattern, we apply the general-to-specific strategy developed by Krolzig and Hendry (2001). This strategy combines ordinary least squares with a computer-automated algorithm that selects a specification based on coefficients' statistical significance, residual properties, and parameter constancy. Then, we derive the testable implications from Hicks' theorem and evaluate them with econometric formulations; we find qualified support for these implications. Having obtained these formulations, we evaluate their ex-post predictive accuracy and compare it to that of an autoregressive model. The key finding is that ignoring movement in relative prices results in a loss of information for predicting aggregation residuals.

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Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 767.

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Date of creation: 2003
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Handle: RePEc:fip:fedgif:767
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  1. Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Econometric Society World Congress 2000 Contributed Papers 0411, Econometric Society.
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