Estimation of Panel Data Models with Mixed Sampling Frequencies*

Standard panel models usually assume that data are available at the same frequency. Occasionally, researchers might work with variables sampled at different frequencies. A common practice is to aggregate all variables to the same frequency by an equal weighting scheme. We show that such a simple aggregation scheme results in biases for common estimators. We propose a data-driven method to determine weights for aggregation. We further demonstrate that, in contrast with single-frequency panel models, the Mundlak device and the Chamberlain's approach lead to different estimators for panels with mixed sampling frequencies. The proposed estimators have satisfying finite sample performances in various simulation designs. As an empirical illustration, we apply the new method to the estimation of the effects of temperature fluctuations on economic growth. The empirical evidence shows that the temperature shocks mainly work through the level effect instead of the growth effect for poor countries.