A Computationally Practical Simulation Estimator for Panel Data
扩展了McFadden的模拟矩估计方法到面板数据，基于转移概率分解开发了高精度模拟方法，蒙特卡洛测试显示其性能优于基于求积的极大似然估计和模拟极大似然，且计算速度快。
In this paper I develop a practical extension of McFadden's method of simulated moments estimator for limited dependent variable models to the panel data case. The method is based on a factorization of the MSM first order condition into transition probabilities, along with the development of a new highly accurate method for similating these transition probabilities. A series of Monte-Carlo tests show that this MSM estimator performs quite well relative to quadrature-based ML estimators, even when large numbers of quadrature points are employed. The estimator also performs well relative to simulated ML, even when a highly accurate method is used to simulate the choice probabilities. In terms of computational speed, complex panel data models involving random effects and ARMA errors may be estimated via MSM in times similar to those necessary for estimation of simple random effects models via ML-quadrature.