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动态面板多项Logit模型AI 解读

This paper provides moment conditions for dynamic multinomial logit panel data models with individual specific fixed effects. The moment conditions do not depend on the fixed effects and thus form the basis for generalized method of moments estimation. For a leading case, we provide sufficient conditions under which the moment conditions identify the common parameters, and we illustrate their use in an empirical application to household soft drink purchases. The construction…

动态网络形成中的同质性与传递性AI 解读

In social and economic networks linked agents often share connections in common. There are two competing explanations for this phenomenon. First, agents may have a structural taste for transitive links - the returns to linking may be higher if two agents share a common connection. Second, agents may assortatively match on unobserved attributes, a process called homophily. We study parameter identifiability in a simple model of dynamic network formation with both effects.…

允许反馈的非线性面板数据模型矩约束AI 解读

Many panel data methods, while allowing for general dependence between covariates and time-invariant agent-specific heterogeneity, place strong a priori restrictions on feedback: how past outcomes, covariates, and heterogeneity map into future covariate levels. Ruling out feedback entirely, as often occurs in practice, is unattractive in many dynamic economic settings. We provide a general characterization of all feedback and heterogeneity robust (FHR) moment conditions for…

面板与网络数据中带一般固定效应的二元 Logit 选择模型AI 解读

This paper systematically analyzes and reviews identification strategies for binary choice logit models with fixed effects in panel and network data settings. We examine both static and dynamic models with general fixed-effect structures, including individual effects, time trends, and two-way or dyadic effects. A key challenge is the incidental parameter problem, which arises from the increasing number of fixed effects as the sample size grows. We explore two main strategies…

动态固定效应 Logit 模型的转移概率与矩条件AI 解读

Dynamic logit models are popular tools in economics to measure state dependence. This paper introduces a new method to derive moment restrictions in a large class of such models with strictly exogenous regressors and fixed effects. We exploit the common structure of logit-type transition probabilities and elementary properties of rational fractions, to formulate a systematic procedure that scales naturally with model complexity (e.g the lag order or the number of observed…

含预定协变量的二元选择面板模型中的识别AI 解读

We study identification in a binary choice panel data model with a single \emph{predetermined} binary covariate (i.e., a covariate sequentially exogenous conditional on lagged outcomes and covariates). The choice model is indexed by a scalar parameter $θ$, whereas the distribution of unit-specific heterogeneity, as well as the feedback process that maps lagged outcomes into future covariate realizations, are left unrestricted. We provide a simple condition under which $θ$ is…

网络中的函数差分AI 解读

Economic interactions often occur in networks where heterogeneous agents (such as workers or firms) sort and produce. However, most existing estimation approaches either require the network to be dense, which is at odds with many empirical networks, or they require restricting the form of heterogeneity and the network formation process. We show how the functional differencing approach introduced by Bonhomme (2012) in the context of panel data, can be applied in network…