用神经网络建模消费习惯，提升需求预测精度与政策评估效果
Neural Demand Estimation with Habit Formation and Rationality Constraints
- 通过状态依赖偏好得分和正则化约束，实现无参数效用的需求估计
- 模拟显示考虑习惯形成可准确恢复弹性，实证中降低33%预测误差
- 适合研究消费行为、福利分析或需动态需求模型的研究者使用
我们提出一种灵活的神经需求系统，用于连续预算分配，通过最小化KL散度估计预算份额。份额由状态依赖偏好得分经softmax生成，并施加单调性、斯卢茨基对称性等正则化约束，确保比较静态与福利分析的一致性，无需预设效用函数形式。状态依赖通过习惯存量建模，即过去消费的指数加权移动平均。模拟结果显示，该方法能准确恢复弹性，且当存在习惯形成时收益显著。在使用Dominick's止痛药数据的实证中，引入习惯使样本外误差降低约33%，重塑替代模式，并使布洛芬价格上升10%带来的消费者剩余损失增加15-16%，相较于静态模型。代码已公开于https://github.com/martagrz/neural_demand_habit。
原文摘要 · Abstract (English)
We develop a flexible neural demand system for continuous budget allocation that estimates budget shares on the simplex by minimizing KL divergence. Shares are produced via a softmax of a state-dependent preference scorer and disciplined with regularity penalties (monotonicity, Slutsky symmetry) to support coherent comparative statics and welfare without imposing a parametric utility form. State dependence enters through a habit stock defined as an exponentially weighted moving average of past consumption. Simulations recover elasticities and welfare accurately and show sizable gains when habit formation is present. In our empirical application using Dominick's analgesics data, adding habit reduces out-of-sample error by c.33%, reshapes substitution patterns, and increases CV losses from a 10% ibuprofen price rise by about 15-16% relative to a static model. The code is available at https://github.com/martagrz/neural_demand_habit .
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