Estimating nonlinear time-series models using simulated vector autoregressions
提出两种基于数值模拟的非线性动态经济模型统计推断方法，蒙特卡洛研究发现小样本下效率较低的估计量均方误差更小，并用该估计量估计了美国实际经济周期模型的参数。
This paper develops two new methods for conducting formal statistical inference in nonlinear dynamic economic models. The two methods require very little analytical tractability, relying instead on numerical simulation of the model's dynamic behaviour. Although one of the estimators is asymptotically more efficient than the other, a Monte Carlo study shows that, for a specific application, the less efficient estimator has smaller mean squared error in samples of the size typically encountered in macroeconomics. The estimator with superior small sample performance is used to estimate the parameters of a real business cycle model using observed US time-series data.