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二元潜变量模型的简便闭式估计AI 解读

Summary This paper develops a closed-form non-parametric estimator of the conditional distribution function for a binary outcome variable given an unobserved latent variable. This type of function is commonly used in models that involve measurement error and dynamic models with agent-specific unobserved heterogeneity. This paper presents a consistent extremum sieve estimator with the following advantages: (i) it has a closed-form expression for all the sieve coefficients,…

选择不可观测时动态结构模型的识别与估计AI 解读

This paper develops identification and estimation methods for dynamic discrete choice models when agents’ actions are unobserved by econometricians. We provide conditions under which choice probabilities and latent state transition rules are nonparametrically identified with a continuous state variable in a single-agent dynamic discrete choice model. Our identification strategy from the baseline model can extend to models with serially correlated unobserved heterogeneity,…

存在误分类与社会互动的二元选择模型：态度同伴效应应用AI 解读

The interaction of economic agents is one of the most important elements in economic analyses. Social interactions on subjective outcomes, behavior, or decisions, are inherently difficult to identify and estimate because these variables are prone to misclassification errors. This paper puts forth a binary choice model with misclassification and social interactions to rectify the misclassification problems in social interactions studies. We achieve the identification of the…

数据不完整时动态离散选择模型的锐识别AI 解读

In many empirical studies, those states that are relevant for forward-looking economic agents to make decisions may not be included in the data to which researchers have access. This problem often arises in the context of declining/booming industries. In this paper, we develop the sharp identified sets of structural parameters and counterfactuals for dynamic discrete choice models when empirical data do not cover realizations of relevant future states. Applying the proposed…

主观预期下的动态决策：结构分析AI 解读

We study dynamic discrete choice models without assuming rational expectations. Agents’ beliefs about state transitions are subjective, unknown, and may differ from their objective counterparts. We show that agents’ preferences and subjective beliefs are identified in both finite and infinite horizon models. We estimate the model primitives via maximum likelihood estimation and demonstrate the good performance of the estimator by Monte Carlo experiments. Using the Panel…

对代理变量误差稳健的生产函数估计AI 解读

This paper proposes a new approach to the identification and estimation of production functions. It extends the literature on the structural estimation of production functions, which dates back to the seminal work of Olley and Pakes (1996), by relaxing the scalar-unobservable assumption about the proxy variables. The key additional assumption needed in the identification argument is the existence of two conditionally independent proxy variables. The proposed generalized…

收入动态中经典永久—暂时模型的半参数估计AI 解读

This paper presents identification and estimation results for a flexible state space model. Our modification of the canonical model allows the permanent component to follow a unit root process and the transitory component to follow a semiparametric model of a higher‐order autoregressive‐moving‐average (ARMA) process. Using panel data of observed earnings, we establish identification of the nonparametric joint distributions for each of the permanent and transitory components…

利用工具变量的非参数识别：完备性的充分条件AI 解读

This paper provides sufficient conditions for the nonparametric identification of the regression function $m\left( \cdot \right)$ in a regression model with an endogenous regressor x and an instrumental variable z . It has been shown that the identification of the regression function from the conditional expectation of the dependent variable on the instrument relies on the completeness of the distribution of the endogenous regressor conditional on the instrument, i.e., $f\left( {x|z} \right)$…

短期删失动态面板模型的半参数识别与估计AI 解读

In this paper, we present a semi‐parametric identification and estimation method for censored dynamic panel data models of short time periods and their average partial effects with only two periods of data. The proposed method transforms the semi‐parametric specification of censored dynamic panel data models into a parametric family of distribution functions of observables without specifying the distribution of the initial condition. Then the censored dynamic panel data…

具有不可观测状态代理变量的动态离散选择模型闭式识别AI 解读

Proxies for unobserved skills and technologies are increasingly available in empirical data. For dynamic discrete choice models of forward-looking agents where a continuous state variable is unobserved but its proxy is available, we derive closed-form identification of the structure by explicitly solving integral equations. In the first step, we derive closed-form identification of Markov components, including the conditional choice probabilities and the law of state…

不可观测变量计量：测量误差模型在产业组织与劳动经济学中的应用AI 解读

This paper reviews the recent developments in nonparametric identification of measurement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics. Measurement error models describe mappings from a latent distribution to an observed distribution. The identification and estimation of measurement error models focus on how to obtain the latent distribution and the measurement error distribution from…

一类紧支撑核积分算子的单射性AI 解读

Injectivity of integral operators is related to completeness conditions of their corresponding kernel functions. Completeness provides a useful way of obtaining nonparametric identification in various models including nonparametric regression models with instrumental variables, nonclassical measurement error models, and auction models, etc. However, the condition is quite abstract for empirical work and lacks a proper economic interpretation. We rely on known results…

成对非可分测量误差模型的识别AI 解读

This paper studies the paired nonseparable measurement error models, where two measurements, X and Y , are produced by mutually independent unobservables, U , V , and W , through the system, X = g ( U,V ) and Y = h ( U,W ). We propose restrictions to identify the marginal distribution of the common component U and the conditional distributions of X and Y given U . Applying this method to twin panel data, we find the following robust reporting patterns for years of education:…

竞争企业数量未知时在线价格竞争模型的识别与估计AI 解读

Summary This paper considers identification and estimation of a general model for online price competition. We show that when the number of competing firms is unknown the underlying parameters of the model can still be identified and estimated employing recently developed results on measurement errors. We illustrate our methodology using UK data for personal digital assistants and employ the estimates to simulate competitive effects. Our results reveal that heightened…

含未观测协变量的非线性动态面板模型：识别与估计AI 解读

This paper considers nonparametric identification of nonlinear dynamic models for panel data with unobserved covariates. Including such unobserved covariates may control for both the individual-specific unobserved heterogeneity and the endogeneity of the explanatory variables. Without specifying the distribution of the initial condition with the unobserved variables, we show that the models are nonparametrically identified from two periods of the dependent variable Yit and…

具有不可分未观测异质性的第一价格拍卖识别AI 解读

We propose a novel methodology for identification of first-price auctions, when bidders’ private valuations are independent conditional on one-dimensional unobserved heterogeneity. We extend the existing literature (Li and Vuong, 1998; Krasnokutskaya, 2011) by allowing the unobserved heterogeneity to be non-separable from bidders’ valuations. Our central identifying assumption is that the distribution of bidder values is increasing in the state. When the state-space is…

含不可观测状态变量的动态模型的非参数识别AI 解读

We consider the identification of a Markov process {Wt,Xt∗} when only {Wt} is observed. In structural dynamic models, Wt includes the choice variables and observed state variables of an optimizing agent, while Xt∗ denotes time-varying serially correlated unobserved state variables (or agent-specific unobserved heterogeneity). In the non-stationary case, we show that the Markov law of motion fWt,Xt∗∣Wt−1,Xt−1∗ is identified from five periods of data Wt+1,Wt,Wt−1,Wt−2,Wt−3. In…

潜在竞标人数未知时的一价拍卖估计：误分类方法AI 解读

In this paper, we consider nonparametric identification and estimation of first-price auction models when N*, the number of potential bidders, is unknown to the researcher, but observed by bidders. Exploiting results from the recent econometric literature on models with misclassification error, we develop a nonparametric procedure for recovering the distribution of bids conditional on the unknown N*. Monte Carlo results illustrate that the procedure works well in practice.…

利用工具变量识别和估计含误分类误差的非线性模型：一般解法AI 解读

This paper provides a general solution to the problem of identification and estimation of nonlinear models with misclassification error in a general discrete explanatory variable using instrumental variables. The misclassification error is allowed to be correlated with all the explanatory variables in the model. It is not enough to identify the model by simply generalizing the identification in the binary case with a claim that the number of restrictions is no less than that…