Abstract
In this paper, the adaptive lasso method is used to screen variables, and different neural network models of seven countries are established by choosing variables. Gross domestic product (GDP) is a function of land area in the country, cultivated land, population, enrollment rate, total capital formation, exports of goods and services, and the general government’s final consumption of collateral and broad money. Based on the empirical analysis of the above factors from 1973 to 2016, the results show that the BP neural network model has better performance based on multiple summary statistics, without increasing the number of parameters and better predicting short-term GDP. In addition, the change and the error of the model are small and have a certain reference value.
Highlights
This paper builds an economic model from the data of 1973–2016 for the Group of Seven (G7)by using the adaptive lasso method and BP neural network models to describe the evolution of the gross domestic product (GDP) of several factors
Algorithm steps of the BP neural network: Step 1: The sample input and output parameters are normalized to the interval [0, 1]; Step 2: Weight and threshold initialization, assign a random value in (−1, 1); Step 3: Calculate the state of the hidden layer of the network and the output value of the output layer; Step 4: Calculate the error between the output value and the actual value; Step 5: Determine whether the sample error is within the acceptable range
By comparing the fitting effect, one can find that the BP neural network model is more efficient than the fractional order model in [8]
Summary
This paper builds an economic model from the data of 1973–2016 for the Group of Seven (G7). Is widely used to forecast economic growth, and experimental results show that the BP neural network model is superior to the G (1, 1) model. We adopt the idea in [15] and the economic model of the BP neural network in [5] to study a group of GDP growth in seven countries. Based on the BP neural network model, this paper conducts the modeling of a group of seven economic growth. On 4 June 2014, the G7 leaders’ meeting was hosted by the European Union, which took place in Brussels, Belgium on the evening of the 4th This was the first time Russia was excluded since joining the group in 1997. We collected data from the years 1973–2016 for the G7 countries for a total of 44 years, and used the eight variables obtained to establish different BP neural network models, which describe the changes in GDP in different countries
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