2019 International Course on Applied Econometrics using R
June 17-21, 捷报比分直播
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.
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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
Wooldridge, Jeffrey M. 2013. Introductory Econometrics: A Modern Approach, 5th Edition. South-Western Cengage Learning, USA. Chapters 1-4, 6, 13 and 14.
Arbia,Giuseppe. 2014. A Primer for Spatial Econometrics: With Applications in R. New York: Palgrave Macmillan.
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?.
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/.
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