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Macroeconomic Forecasting Using Genetic Programming Based Vector Error Correction Model
Abstract
Vector autoregressions are widely used in macroeconomic forecasting since they became known in the 1970s. Extensions including vector error correction models, co-integration and dynamic factor models are all rooted in the framework of vector autoregression. The three important extensions are demonstrated to have formal equivalence between each other. Above all, they all emphasize the importance of “common trends” or “common factors”. Many researches, including a series of work of Stock and Watson, find that “common factor” models significantly improve accuracy in forecasting macroeconomic time series. This study follows the work of Stock and Watson. The authors propose a hybrid framework called genetic programming based vector error correction model (GPVECM), introducing genetic programming to traditional econometric models. This new method could construct common factors directly from nonstationary data set, avoiding differencing the original data and thus preserving more information. The authors’ model guarantees that the constructed common factors satisfy the requirements of econometric models such as co-integration, in contrast to the traditional approach. Finally but not trivially, their model could save lots of time and energy from repeated work of unit root tests and differencing, which they believe is convenient for practitioners. An empirical study of forecasting US import from China is reported. The results of the new method dominates those of the plain vector error correction model and the ARIMA model.
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