2012Science Technology and EngineeringRequires access

Short-term Prediction Based on ARIMA Model of GDP in China

Li Wang

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Abstract

Due to a lot of factors influence GDP,and these factors are often multicollinearity,so to accuratly find out the important influencing factors of GDP and modeling is more difficult.And economic data is often self correlation and non-stationary.the ARIMA model is used to fit the 1991 to 2010 GDP data and to make prediction of GDP.The results showed that the ARIMA(1,1,1) is capable of fitting the GDP data,projections indicate that China's economic situation is good.

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What this paper is about

Due to a lot of factors influence GDP,and these factors are often multicollinearity,so to accuratly find out the important influencing factors of GDP and modeling is more difficult.And economic data is often self correlation and non-stationary.the ARIMA model is used to fit the 1991 to 2010 GDP data and to make prediction of GDP.The results showed that the ARIMA(1,1,1) is capable of fitting the GDP data,projections indicate that China's economic situation is good.

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Available abstract

Due to a lot of factors influence GDP,and these factors are often multicollinearity,so to accuratly find out the important influencing factors of GDP and modeling is more difficult.And economic data is often self correlation and non-stationary.the ARIMA model is used to fit the 1991 to 2010 GDP data and to make prediction of GDP.The results showed that the ARIMA(1,1,1) is capable of fitting the GDP data,projections indicate that China's economic situation is good.

Key concepts: Autoregressive integrated moving average, Multicollinearity, Econometrics, Term (time), Real gross domestic product, Economics, China, Statistics

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