• Graduate Program
    • Why study Business Data Science?
    • Research Master
    • Admissions
    • Course Registration
    • Facilities
  • Summer School
  • Research
  • News
  • Events
    • Events Calendar
    • Events archive
    • Tinbergen Institute Lectures
    • Summer School
      • Deep Learning
      • Economics of Blockchain and Digital Currencies
      • Foundations of Machine Learning with Applications in Python
      • Machine Learning for Business
      • Tuition Fees and Payment
      • Tinbergen Institute Summer School Program
    • Annual Tinbergen Institute Conference archive
  • Alumni
  • Magazine
Home | Events Archive | Factor-augmented functional regression with an application to electricity price curve forecasting
Seminar

Factor-augmented functional regression with an application to electricity price curve forecasting


  • Location
    Erasmus University Rotterdam, Mandeville building, room T3-24
    Rotterdam
  • Date and time

    December 14, 2023
    12:00 - 13:00

Abstract
We propose a function-on-function regression model for time-dependent curve data that is consistently estimated by imposing factor structures on the regressors. A novel integral operator $D$ identifies the predictive low-dimensional component with associated factors that are guaranteed to be correlated with the dependent variable. In order to consistently estimate the correct number of factors for each regressor, we introduce a functional eigenvalue difference test. The model is applied to forecast electricity price curves on three different energy markets. We show that the prediction accuracy of the factor-augmented functional regression is comparable to popular machine learning approaches while it provides interpretable insights into correlation structures of electricity prices.