Conditional Beta Estimation and Forecasting with Panel Data Methods

Standard approaches to the estimation and testing of conditional CAPM models with time-varying or random beta have ignored the potential panel nature of financial data. We test for whether or not homogeneity restrictions on the time-variation component of multifactor betas and on the slope parameters for the conditioning variables can be rejected. We find that such homogeneity restrictions are not rejected, and show that there are resultant benefits for testing conditional CAPM and forecasting expected returns and beta. Further, this panel approach yields more precise parameter estimates, and a greater understanding of the significance of both conditional variables and multi-factors