We consider statistical inference in factor analysis for ergodic and non-ergodic diffusion processes from discrete observations. Factor model based on high frequency time series data has been mainly discussed in the field of high dimensional covariance matrix estimation. In this field, the method based on principal component analysis has been mainly used. However, this method is effective only for high dimensional model. On the other hand, there is a method based on the quasi-likelihood. However, since the factor is assumed to be observable, we cannot use this method when the factor is latent. Thus, the existing methods are not effective when the factor is latent and the dimension of the observable variable is not so high. Therefore, we propose an effective method in the situation.
翻译：我们认为,根据高频率时间序列数据得出的系数模型主要在高维共变矩阵估算领域讨论。在这一领域,主要组成部分分析方法主要使用,但这种方法只对高维模型有效。另一方面,有一种基于准相似性的方法。然而,由于假定该系数是可观测的,因此当该系数具有潜伏性时,我们不能使用这种方法。因此,当该系数具有潜伏性,且可观测变量的维度不那么高时,现有方法无效。因此,我们建议一种有效的方法。
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