Estimation of Heterogeneous Panel Data Models With Mixed Sampling Frequencies
研究了混合频率面板数据中异质性参数的估计问题，提出了均值组估计量和相关随机效应估计量，并通过蒙特卡洛模拟和气温对经济增长影响的实证案例验证了方法。
ABSTRACT 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.