Spectral estimation for mixed causal-noncausal autoregressive models
提出基于三阶谱累积量的方法，在无需假设误差分布的情况下识别和估计混合因果-非因果自回归模型，蒙特卡洛实验和八种商品价格实证表明该方法能有效捕捉金融泡沫中的局部爆炸性动态。
.Mixed causal-noncausal autoregressive (MAR) processes driven by non Gaussian noise can replicate the non linear dynamics induced by local explosive episodes observed in financial bubbles. MAR models cannot be identified using second-order moments because they share spectral density with a set of different representations. In this study, we propose an identification and estimation method based on the third-order spectral density cumulant that can recover the complete probability structure of the errors without assuming any prior knowledge of the probability distribution function. Monte Carlo experiments demonstrated the estimation and identification performances. Furthermore, we illustrated the adequacy of our method through an empirical application to eight monthly commodity prices. The results show that MAR models can effectively capture the explosiveness and bubble phenomena generated in the commodities market.