Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach
AbstractDevelopments 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
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Date of creation: 11 Aug 2004
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Leading indicators; Global electronics cycle; VAR; Forecasting;
Other versions of this item:
- Hwee Kwan Chow & Keen Meng Choy, 2004. "Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach," Departmental Working Papers wp0407, National University of Singapore, Department of Economics.
- Hwee Kwan Chow & Keen Meng Choy, 2004. "Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach," Working Papers 16-2004, Singapore Management University, School of Economics.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-10-30 (All new papers)
- NEP-BEC-2004-10-30 (Business Economics)
- NEP-ETS-2004-10-30 (Econometric Time Series)
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