This paper details an innovative methodology to integrate image data into
traditional econometric models. Motivated by forecasting sales prices for
residential real estate, we harness the power of deep learning to add
"information" contained in images as covariates. Specifically, images of homes
were categorized and encoded using an ensemble of image classifiers (ResNet-50,
VGG16, MobileNet, and Inception V3). Unique features presented within each
image were further encoded through panoptic segmentation. Forecasts from a
neural network trained on the encoded data results in improved out-of-sample
predictive power. We also combine these image-based forecasts with standard
hedonic real estate property and location characteristics, resulting in a
unified dataset. We show that image-based forecasts increase the accuracy of
hedonic forecasts when encoded features are regarded as additional covariates.
We also attempt to "explain" which covariates the image-based forecasts are
most highly correlated with. The study exemplifies the benefits of
interdisciplinary methodologies, merging machine learning and econometrics to
harness untapped data sources for more accurate forecasting.

该研究介绍了一种创新的方法，将图像数据集成到传统计量模型中，通过深度学习将图像中包含的信息作为协变量，用于预测房地产销售价格，通过图像编码和分割的方式提高了预测准确性。

利用图像作为协变量：用深度学习测量路边吸引力

Using Images as Covariates: Measuring Curb Appeal with Deep Learning

We discuss the applications of Random Matrix Theory in the context of
financial markets and econometric models, a topic about which a considerable
number of papers have been devoted to in the last decade. This mini-review is
intended to guide the reader through various theoretical results (the
Marcenko-Pastur spectrum and its various generalisations, random SVD, free
matrices, largest eigenvalue statistics, etc.) as well as some concrete
applications to portfolio optimisation and out-of-sample risk estimation.

本文讨论了随机矩阵理论在金融市场和计量经济模型中的应用，涵盖了许多理论结果和具体应用，如马尔琴科 - 帕斯图尔谱及其各种扩展、随机 SVD、自由矩阵、最大特征值统计等，并着重阐述了该理论与投资组合优化和风险评估的关系。