A graduate-level first course covering certain basic topics in non-parametric econometrics and resampling (bootstrap), suitable for students and researchers in a wide variety of fields (including economics, statistics/Bio-statistics/mathematical statistics, forest management, engineering).

Course main content

  1. Basic notions in probability, statistics and statistical data
  2. Non-parametric density estimation
  3. Non-parametric regression
  4. Derivative estimation
  5. Density and regression with mixed data
  6. Nearest neighbour methods
  7. Semi-parametric regression
  8. Resampling and bootstrap inference

Learning Outcomes

On completion of the course, the students shall:
  • have developed a basic understanding of the set up and language of non-parametric econometric methods, with particular focus on density estimation and regression
  • have developed an understanding of the basic mechanics of asymptotic properties of certain non-parametric econometric methods (with particular focus on density estimation and regression)
  • have acquired an ability to understand the basic numerical issues involved and to solve (estimate) basic non-parametric methods in any software package (preferably in the R package)
  • have developed an ability to understand the need for, benefits of, resampling methods, in particular, the bootstrap
  • have acquired some idea about the validity of the bootstrap and its use and implementation, in certain regression settings
pdf Read more about the course, prerequisites, literature, schedule, grading, contact and information on how to enrol.

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