Non Parametric methods are statistical techniques that do not require to specify functional forms for objects being estimated. Instead, we let the data itself plays and informs the resulting model in a particular manner. Such methods are becoming increasingly popular for applied data analysis, they are best suited to situations involving large data sets for which the number of variables involved is manageable.

These methods are often deployed after common parametric specifications are found to be unsuitable for the problem at hand, particularly when formal rejection of a parametric model based on specification tests yields no clues as to the direction in which to search for an improved parametric model.

Today the real data are very complex and the DGP asks the researcher to deal with a lot of non linearities in the parameter of interest (density function, regression function, volatility etc, all these objects being elements of infinite dimensional functional spaces). The job market understood this necessity and almost any serious software contains the principal techniques for non parametric estimation.

We illustrate the different models and techniques with examples built in R and Matlab: R because of the huge number of packages from CRAN, and the second because is the easiest environment for programming arrays in econometrics (and typically all objects are arrays in applied econometrics). Both are very representative for the job market.

The course will not contain the teaching of programming techniques since it shall be subject to a standard exam of 1.5h (supposed to verify the acquisition of the concepts and the theoretical results). However some skills will be presented and links will be provided in order to ensure a personal homework of each student.