Estimation of Heterogeneous Panel Data Models with Mixed Sampling Frequencies

This paper studies heterogeneous mixed-frequency panel data models, focusing on linear specifications that allow heterogeneity in both aggregation weights and slope coefficients. To address potential correlations between the heterogeneity and the covariates, we first show that, under additional normalization conditions, the mean-group estimator delivers consistent and asymptotically normal slope estimates but biased weights estimators. As an alternative, we propose a correlated random effects estimator using a generalized Mundlak specification. We further discuss the implementation of these two estimators when covariates are observed at substantially higher sampling frequencies. Monte Carlo simulations are conducted to assess their finite-sample properties. As an empirical illustration, we revisit the impact of temperature fluctuations on economic growth using the proposed framework.