• Graduate program
    • Courses
    • Why Tinbergen Institute?
    • Program Structure
    • Course Registration
    • Recent PhD Placements
    • Admissions
    • Facilities
  • Research
  • News
  • Events
    • Events Calendar
    • Tinbergen Institute Lectures
    • Annual Tinbergen Institute Conference
    • Events Archive
    • Crash Course in Experimental Economics
    • Behavioral Macro and Complexity
    • Introduction in Genome-Wide Data Analysis
    • Econometric Methods for Forecasting and Data Science
  • Times
Home | Events Archive | Random Forests: An Introduction from an Econometrics Perspective
Master's Thesis defense

Random Forests: An Introduction from an Econometrics Perspective

  • Series
  • Speaker
    Anna Buijsman
  • Field
  • Location
    Room 1.60
  • Date and time

    August 23, 2019
    10:30 - 11:30

The overlap between Econometrics and Machine Learning is still at its infancy. This paper contributes to connecting the two fields by providing an overview which explains the Random Forests algorithm and its performance combined with highlighted results on the asymptotic properties and the implications of these results for econometric research. The conclusion is that Random Forests performs well in prediction problems, so the algorithm is useful when econometric problems can be fitted into a prediction type framework. However, estimating standard errors, confirming theoretic asymptotic properties and doing inference are not straightforward and still contain a lot of open questions.