• 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
      • Marketing Research with Purpose
      • Sustainable Finance
      • Tuition Fees and Payment
      • Tinbergen Institute Summer School Program
    • Annual Tinbergen Institute Conference archive
  • Alumni
  • Magazine
Home | Events | Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels
Seminar

Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels

  • Location
    Erasmus University Rotterdam, Campus Woudestein, ET-14
    Rotterdam
  • Date and time

    March 06, 2025
    12:00 - 13:00

Abstract

We develop a nonparametric, kernel-based joint estimator for conditional mean and covariance matrices in large and unbalanced panels. The estimator is supported by rigorous consistency results and finite-sample guarantees, ensuring its reliability for empirical applications in Finance. We apply it to an extensive panel of monthly US stock excess returns from 1962 to 2021, using macroeconomic and firm-specific covariates as conditioning variables. The estimator effectively captures time-varying cross-sectional dependencies, demonstrating robust statistical and economic performance. We find that idiosyncratic risk explains, on average, more than 75\% of the cross-sectional variance.