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发布时间: 2018-05-22  


Course on Applied Econometrics using R



June 11-15, School of Environment, BNU


Laixiang Sun is a Professor in the Department of Geographical Sciences, University of Maryland. He was awarded the title “Fellow (Academician) of the Academy of Social Sciences”. He is the Academic Master of 111 Plan in BNU. He has produced more than 140 research publications in regional sciences and regional economics, environmental sciences and management, business and management studies, integrated modelling, and ecological economics.

孙来祥教授,马里兰大学地理科学系教授,英国社科研究院院士,北京师范大学高等学校学科创新引智基地(111计划)学术大师。其研究领域包括区域科学和区域经济学、气候变化减缓和适应、综合模拟和应用系统分析、空间经济学、生态经济和发展经济学等。孙来祥教授在Nature communication,PNAS等期刊上发表论文140篇以上。


报名信息请发送到 liugengyuan@bnu.edu.cn。 后续课程信息将由邮件通知。



Applied Spatial Econometrics using R

Laixiang Sun (University of Maryland, College Park, USA)


This course will use the popular open source statistical computer language R. Its focus is on using statistical computing to produce analytical reports for real-world applications, research papers, and dissertations. Its aim is to enable students to develop the application of statistics to the study of economic geography, to understand how these techniques can help them comprehend the complex reality concerned and endow them with a fascination for spatial econometric methods. Through lectures, group work, and hands-on computer sessions, the class will enable students to explain when and why to use spatial econometrics and demonstrate how to apply spatial econometric methods. The class will help students develop ability to estimate and interpret spatial econometric models for analyzing socioeconomic relationships and human-environment interactions. The class will enable students to use spatial econometric tools in R effectively.


Textbooks & Key References

  1. Wooldridge, Jeffrey M. 2013. Introductory Econometrics: A Modern Approach, 5th Edition. South-Western Cengage Learning, USA. Chapters 1-4, 6, 13 and 14.
  2. Arbia,Giuseppe. 2014. A Primer for Spatial Econometrics: With Applications in R . New York: Palgrave Macmillan.
  3. Ward, Michael D. and Gleditsch, Kristian Skrede. 2008. Quantitative Applications in the Social Sciences: Spatial Regression Models . SAGE Publications Inc. A pre-published version of this book (2007), entitled An Introduction to Spatial Regression Models in the Social Sciences , can be downloaded at https://web.duke.edu/methods/pdfs/SRMbook.pdf?.
  4. Millo, G. and Piras, G. 2012. splm: Spatial Panel Data Models in R. Journal of Statistical Software , 47(1), 1-38. URL http://www.jstatsoft.org/v47/i01/.
  5. De Micheaux, P. L, Drouilhet, R. and Liquet, B. 2013. The R Software: Fundamentals of Programming and Statistical Analysis . New York: Springer.


Topics (topics in parentheses may be skipped or added, dependent on the level of students)

  • The Nature of Econometrics and Economic Data
  • Introduction to R
  • The Simple Regression Model
  • Multiple Regression Analysis I: Estimation
  • Multiple Regression Analysis II: Inference
  • Spatial Dependence, Measuring Spatial Association & Correlation
  • Spatially Lagged Dependent Variables and Spatially Lagged Model
  • Spatial Error Model 
  • Pooling Cross Sections across Time: Simple Panel Data Methods
  • Fixed-effect versus Random Effects in Panel Data Estimation
  • Spatial Lag Model of Panel Data
  • Spatial Error Model of Panel Data


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