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多重均衡与不可观测异质性下不完全信息博弈的识别AI 解读

This paper deals with identification of discrete games of incomplete information when we allow for three types of unobservables: payoff‐relevant variables, both players' private information and common knowledge, and nonpayoff‐relevant variables that determine the selection between multiple equilibria. The specification of the payoff function and the distributions of the common knowledge unobservables is nonparametric with finite support (i.e., finite mixture model). We…

照料年迈父母与欧洲成熟女性的就业状态AI 解读

We study the prevalence of informal caregiving to elderly parents by their mature daughters in Europe and the links between parental health, intense (daily) caregiving, and the employment status of daughters. We group data from SHARE into three country pools (North, Central, and South), which differ in the availability of public formal care services and female labor market attachment. There is a strong North-South gradient in the (positive) effect of parental ill health on…

动态离散选择结构模型：综述AI 解读

This paper reviews methods for the estimation of dynamic discrete choice structural models and discusses related econometric issues. We consider single-agent models, competitive equilibrium models and dynamic games. The methods are illustrated with descriptions of empirical studies which have applied these techniques to problems in different areas of economics. Programming codes for some of the estimation methods are available in a companion web page.

动态离散博弈的序贯估计AI 解读

This paper studies the estimation of dynamic discrete games of incomplete information. Two main econometric issues appear in the estimation of these models: the indeterminacy problem associated with the existence of multiple equilibria, and the computational burden in the solution of the game. We propose a class of pseudo maximum likelihood (PML) estimators that deals with these problems and we study the asymptotic and finite sample properties of several estimators in this…

交换嵌套不动点算法：离散马尔可夫决策模型的一类估计量AI 解读

This paper proposes a procedure for the estimation of discrete Markov decision models and studies its statistical and computational properties.Our Nested Pseudo-Likelihood method (NPL) is similar to Rust's Nested Fixed Point algorithm (NFXP), but the order of the two nested algorithms is swapped.First, we prove that NPL produces the Maximum Likelihood Estimator under the same conditions as NFXP.Our procedure requires fewer policy iterations at the expense of more…