README FILE FOR [Modeling Chinese Consumer Confidence using Google Trend Data: crowd-sourcing econometrics.]
Created by:
[Xianlei Dong]
[No.100, Pingleyuan, Chaoyang District]
[Beijing, China, 100124]
[sddongxianlei@163.com]
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FILE LIST

[
File.1. CCI data-manual-092013.xlsx
File.2. CCI data-manual-062014.xlsx
File.3. Google trends download data-manual-092013.xlsx
File.4. Google trends download data-manual-062014.xlsx
]



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FILE INFORMATION
[
1. CCI data are provided by the National Bureau of Statistics of the People's Republic of China (http://www.stats.gov.cn/). This data can also be obtained from Sina (http://finance.sina.com.cn/mac/#boom-4-0-32-2).
2. Google trends download data are downloaded from Google trends (www.google.com/trends/). All data was manually downloaded.
3. StataSE.11 is used to analyze the data.
4. File.1 and file.3 are used to model 2006m1 to 2013m6. File.2 and file.4 are used to evaluate from 2013m7 to 2014m5.
5. Google trends time series can be weekly or monthly. We list all the weekly data first and the monthly data after. 
6. Google trends data is continuously re-normalized. There may be minor differences between file.3 and file.4 since we retrieved the data in file.3 on September 2013, while we obtained the data in file.4 on June 2014.
   

]

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RESEARCH QUESTION(S)
We propose that the large-scale interactions of a nation’s citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.

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COPYRIGHT & LICENSING INFORMATION
Source data was created by [Xianlei Dong].
This data is licensed for reuse under a Creative Commons Attribution 3.0  license. 

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LIMITATIONS
Our analysis is premised on the validity of the Google Trends data.