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Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach

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  • Keen Meng Choy
  • Hwee Kwan Chow

Abstract

Developments in the global electronics industry are typically monitored by tracking indicators that span a whole spectrum of activities in the sector. However, these indicators invariably give mixed signals at each point in time, thereby hampering efforts at prediction. In this paper, we present a unified framework for forecasting the global electronics cycle by constructing a VAR model that captures the economic interactions between leading indicators representing expectations, investments, orders, inventories and prices. The ability of the indicators to presage world semiconductor sales is assessed by Granger causality tests. The VAR model is also used to derive the dynamic paths of adjustment of global chip sales in response to shocks in each of the leading variables. These impulse response functions conform to our theoretical priors and confirm the leading quality of the selected indicators. Finally, out-of-sample forecasts of global chip sales are generated from the VAR model and compared with predictions from a univariate model as well as a model which uses a composite index of the leading indicators. An evaluation of their relative accuracy suggests that the VAR model's forecasting performance is superior to that of the univariate model and comparable to that of the composite index model

Suggested Citation

  • Keen Meng Choy & Hwee Kwan Chow, 2004. "Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach," Econometric Society 2004 Australasian Meetings 223, Econometric Society.
  • Handle: RePEc:ecm:ausm04:223
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    1. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    2. Giuseppe Parigi & Roberto Golinelli & Giorgio Bodo, 2000. "Forecasting industrial production in the Euro area," Empirical Economics, Springer, vol. 25(4), pages 541-561.
    3. Cheung, Yin-Wong & Lai, Kon S, 1995. "Lag Order and Critical Values of a Modified Dickey-Fuller Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 57(3), pages 411-419, August.
    4. Elliott, Graham, 1999. "Efficient Tests for a Unit Root When the Initial Observation Is Drawn from Its Unconditional Distribution," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(3), pages 767-783, August.
    5. Christopher A. Sims & Tao Zha, 1999. "Error Bands for Impulse Responses," Econometrica, Econometric Society, vol. 67(5), pages 1113-1156, September.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Bart Hobijn & Kevin J. Stiroh & Alexis Antoniades, 2003. "Taking the pulse of the tech sector: a coincident index of high-tech activity," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 9(Oct).
    8. Gonzalo Camba-Mendez & George Kapetanios & Richard J. Smith & Martin R. Weale, 2001. "An automatic leading indicator of economic activity: forecasting GDP growth for European countries," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-37.
    9. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," Review of Economic Studies, Oxford University Press, vol. 53(4), pages 671-690.
    10. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    11. Perron, Pierre & Rodriguez, Gabriel, 2003. "GLS detrending, efficient unit root tests and structural change," Journal of Econometrics, Elsevier, vol. 115(1), pages 1-27, July.
    12. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    13. Koch, Paul D & Rasche, Robert H, 1988. "An Examination of the Commerce Department Leading-Indicator Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 167-187, April.
    14. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    15. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
    16. Veloce, William, 1996. "An evaluation of the leading indicators for the Canadian economy using time series analysis," International Journal of Forecasting, Elsevier, vol. 12(3), pages 403-416, September.
    17. Zarnowitz, Victor, 1992. "Business Cycles," National Bureau of Economic Research Books, University of Chicago Press, number 9780226978901, July.
    18. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
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    Keywords

    Leading indicators; Global electronics cycle; VAR; Forecasting;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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