Sieve bootstrap inference for linear time-varying coefficient models
提出筛子自助法框架，用于基于局部线性估计的时变系数回归模型的点推断和同时推断，并应用于欧洲碳排放交易体系中CO2证书价格与基本面驱动因素关系的时变性分析。
We propose a sieve bootstrap framework to conduct pointwise and simultaneous inference for time-varying coefficient regression models based on a local linear estimator. The asymptotic validity of the sieve bootstrap in the presence of autocorrelation is established. The bootstrap automatically produces a consistent estimate of nuisance parameters, both at the interior and boundary points. In addition, we develop a bootstrap-based test for parameter constancy and examine its asymptotic properties. An extensive simulation study demonstrates a good finite sample performance of our methods. The proposed methods are applied to assess the price development of CO<sub>2</sub> certificates in the European Emissions Trading System. We find evidence of time variation in the relationship between allowance prices and their fundamental price drivers. The time variation might offer an explanation for previous contradicting findings using linear regression models with constant coefficients.