We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices. To achieve this, we generate abstract product attributes (or ``features'') from descriptions and images using deep neural networks. These attributes are then used to estimate the hedonic price function. To demonstrate the effectiveness of this approach, we apply the models to Amazon's data for first-party apparel sales, and estimate hedonic prices. The resulting models have a very high out-of-sample predictive accuracy, with $R^2$ ranging from $80\%$ to $90\%$. Finally, we construct the AI-based hedonic Fisher price index, chained at the year-over-year frequency, and contrast it with the CPI and other electronic indices.
翻译：我们开发了能够高效处理大量非结构化产品数据（文本、图像、价格、数量）的实证模型，以生成准确的享乐价格估计值及衍生指数。为实现这一目标，我们利用深度神经网络从产品描述和图像中提取抽象产品属性（或称“特征”）。这些属性随后被用于估计享乐价格函数。为验证该方法的有效性，我们将模型应用于亚马逊自营服装销售数据，并估算了享乐价格。所得模型具有极高的样本外预测精度，其 $R^2$ 值介于 $80\%$ 至 $90\%$ 之间。最后，我们构建了基于人工智能的享乐费雪价格指数（按年同比频率链式计算），并将其与消费者价格指数及其他电子指数进行对比分析。
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