Estimation of Panel Data Models with Mixed Sampling Frequencies*
研究了面板数据中变量采样频率不同时的估计问题，提出一种数据驱动的加权聚合方法，并应用于温度波动对经济增长影响的实证分析。
Abstract 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.