Forecast Evaluation Under Asymmetric Loss: A Monte Carlo Analysis of the EKT Method
通过蒙特卡洛实验检验Elliott等人提出的预测评估方法，发现该方法能精确估计损失函数的非对称程度，且J检验在正确模型下尺寸接近名义水平，对错误模型有高检验力。
Abstract This paper contributes to the literature on forecast evaluation by conducting an extensive Monte Carlo experiment using the evaluation procedure proposed by Elliott, Komunjer and Timmermann. We consider recent developments in weighting matrices for GMM estimation and testing. We pay special attention to the size and power properties of variants of the J ‐test of forecast rationality. Proceeding from a baseline scenario to a more realistic setting, our results show that the approach leads to precise estimates of the degree of asymmetry of the loss function. For correctly specified models, we find the size of the J ‐tests to be close to the nominal size, while the tests have high power against misspecified models. These findings are quite robust to inducing fat tails, serial correlation and outliers.