1. Data collection technology: This includes methods such as questionnaire survey, interview, observation and experiment. These methods can help researchers collect the raw data they need.
2. Data processing tools: This includes Excel, SPSS, SAS, R and other statistical software. These tools can help researchers clean, organize and transform the collected data, making it suitable for further analysis.
3. Data analysis technology: This includes descriptive statistical analysis, inferential statistical analysis, regression analysis, cluster analysis, factor analysis, time series analysis and other methods. These methods can help researchers discover the laws and trends from the data and verify the research hypothesis.
4. Drawing tools: including Excel, PowerPoint, Tableau and other software. These tools can help researchers to display the analysis results in an intuitive chart form, making it easier for readers to understand.
5. Literature review tools: This includes software such as EndNote and Mendeley. These tools can help researchers manage and reference a large number of documents.
6. Model building tools: This includes software such as Stata and Eviews. These tools can help researchers build and estimate various economic models, such as regression models and time series models.
7. Programming tools: This includes programming languages such as Python and R, which can help researchers with complex data processing and analysis, such as machine learning and deep learning.
8. Report writing tools: including software such as Word and LaTeX. These tools can help researchers write clear and standardized research reports.
The above are some commonly used techniques and tools in empirical analysis. Different research projects may need to use different technologies and tools, and researchers need to choose appropriate technologies and tools according to actual needs.