Macroeconomic Forecasting Using Genetic Programming Based Vector Error Correction Model

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Xi Chen and Ye Pang and Guihuan Zheng",
  title =        "Macroeconomic Forecasting Using Genetic Programming
                 Based Vector Error Correction Model",
  booktitle =    "Buisness Intelligence in Economic Forcasting",
  publisher =    "IGI Global",
  year =         "2010",
  editor =       "Jue Wang and Shouyang Wang",
  chapter =      "1",
  pages =        "1--15",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "9781615206292",
  DOI =          "doi:10.4018/978-1-61520-629-2",
  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 emphasise 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.",
  notes =        "

                 Xi Chen (Deloitte Financial Advisory Services, China),
                 Ye Pang (The People's Insurance Company (Group) of
                 China Limited, China), and Guihuan Zheng (The People's
                 Bank of China, China)",
  size =         "15 pages",

Genetic Programming entries for Xi Chen Ye Pang Guihuan Zheng