Estimating Intertemporal Quadratic Adjustment Cost Models with Integrated Series
提出一种多阶段方法，利用数据整合阶数的预检验信息，改进欧拉方程中非平稳序列的参数估计，并解释折扣率和调整成本估计不佳的常见现象。
We consider the estimation of parameters in Euler equations where regressand and regressors may be nonstationary, and propose a several-stage procedure requiring only knowledge of the Euler equation and the order of integration of the data. This procedure uses the information gained from pre-testing for the order of integration of data series to improve specification and estimation. We can also offer an explanation of the frequent empirical finding that discount rates and adjustment costs are poorly estimated. Both analytical and experimental (Monte Carlo) results are provided.