Academic and non-academic investments at university: The role of expectations, preferences and constraints
大学中的学业与非学业投入:预期、偏好和约束的作用AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要明确估计学生时间分配的离散选择模型,并模拟约束对投入差距的作用。
大学中的学业与非学业投入:预期、偏好和约束的作用AI 解读 ↓
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This paper estimates a discrete choice model of time allocation decisions made by university students. We consider investments in academic and non-academic activities, such as job placements or volunteering. Identification is achieved using data collected through a recent survey of UK university students on subjective expectations about the returns to these activities, and the enjoyment students derive from them. Unobserved heterogeneity in the choice set is addressed using a sufficient set logit method. The analysis reveals significant ethnic differences in the level of investments, expected academic and labour market returns, and enjoyment of academic and non-academic activities. Simulations suggest that existing constraints play an important role in explaining ethnic gaps in investments.
扰动效用模型的 Fenchel–Young 估计量
Perturbed Utility Models (PUMs) represent discrete choice as utility maximization under convex perturbation or regularization, encompassing a broad class of choice models, including Multinomial Logit (MNL) and models that permit zero choice probabilities. Because, for general perturbations, maximum likelihood may be non-convex, and the negative log-likelihood is infinite when a sparse model assigns zero probability to an observed choice, we specialize the Fenchel--Young…
熵正则化最优传输下的部分识别
In many statistical settings, the available data and maintained assumptions do not suffice to uniquely identify the model parameters of interest. In such cases, one can only identify sets which are guaranteed to contain the true parameters. These are often characterized through linear programs that optimize over models compatible with the observed data. These programs can be infinite-dimensional in the optimizer and the number of constraints. We provide a unified way to…
劳动者侧歧视:信念与偏好
When agents choose among options with unobserved attributes to maximize expected utility, their choices conflate preferences with beliefs about those attributes. We develop a novel theory-driven identification strategy for such settings, showing that compensating differentials separately identify beliefs from preferences. Noting that workers’ preferences and beliefs shape labor market outcomes, yet remain understudied, we apply our method to provide the first evidence on the…
BLP 模型中的条件矩估计与推断
The random-coefficient demand model of Berry, Levinsohn, and Pakes (1995) is commonly estimated by the generalized method of moments (GMM), using an unconditional moment restriction with a fixed set of instruments. Identification of the model, however, rests on a conditional moment restriction. The two are not equivalent: the unconditional restriction may admit additional parameter values. We construct a counterexample in which the model is identified by the conditional…
可变选择集下的需求估计:嵌套 Logit 的似然修正
We derive the exact likelihood of the Berry (1994) share-form nested logit and show it contains a Jacobian term that depends on nest size and the nesting parameter. Omitting the term biases within-nest substitution estimates wherever choice sets vary across markets. Commonly-used count instruments for the within-nest share fail exclusion when product counts enter demand directly. The corrected likelihood identifies substitution without them, making crowding estimable. We…
动态离散选择与逆强化学习:从人类行为推断偏好和信念
This article surveys two deeply connected literatures that approach the same fundamental problem from different disciplinary traditions: dynamic discrete choice (DDC) in structural econometrics and inverse reinforcement learning (IRL) in machine learning. Both seek to infer the preferences of decision makers from observed sequential behavior, assuming that individuals act to maximize an expected reward function within a dynamic, uncertain environment formalized as a Markov…