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Forecasting the semiconductor industry cycles by bootstrap prediction intervals

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Abstract:

In recent years, there has been a recognition that point forecasts of the semiconductor industry sales may often be of limited value. There is substantial interest for a policy maker or an individual investor in knowing the degree of uncertainty that attaches to the point forecast before deciding whether to increase production of semiconductors or purchase a particular share from the semiconductor stock market. In this article, I first obtain the bootstrap prediction intervals of the global semiconductor industry cycles by a vector autoregressive (VAR) model using monthly US data consisting of four macroeconomic and seven industry-level variables with 92 observations. The 24-step-ahead out-of-sample forecasts from various VAR setups are used for comparison. The empirical result shows that the proposed 11-variable VAR model with the appropriate lag length captures the cyclical behaviour of the industry and outperforms other VAR models in terms of both point forecast and prediction interval.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/00036840600706995

Affiliations: Institute of International Economics, National Chung Cheng University, Chiayi 621, Taiwan

Publication date: July 1, 2007

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