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Factor models are commonly estimated using methods such as Principal Component Analysis (PCA) and Factor Analysis. PCA reduces the dimensionality of data by identifying the principal components that explain the most variance, while Factor Analysis aims to identify underlying relationships between observed variables. Additionally, Maximum Likelihood Estimation (MLE) can be employed to estimate the parameters of factor models, allowing for inference about the latent factors. These methods help in understanding the structure of the data and the influence of unobserved variables.

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