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健康的状态依赖与异质性：偏差校正固定效应估计AI 解读

SUMMARY This paper estimates a dynamic ordered probit model of self‐assessed health with two fixed effects: one in the linear index equation and one in the cut‐points. This robustly controls for heterogeneity in unobserved health status and in reporting behavior, although we cannot separate both sources of heterogeneity. We find important state dependence effects, and small but significant effects of income and other socioeconomic variables. Having dynamics and flexibly…

具有最大异质性的动态二元结果模型AI 解读

Most econometric schemes to allow for heterogeneity in micro behavior have two drawbacks: they do not fit the data and they rule out interesting economic models. In this paper we consider the time homogeneous first order Markov (HFOM) model that allows for maximal heterogeneity. That is, the modeling of the heterogeneity does not impose anything on the data (except the HFOM assumption for each agent) and it allows for any theory model (that gives a HFOM process for an…

动态离散选择模型中的异质性AI 解读

We consider dynamic discrete choice models with heterogeneity in both the levels parameter and the state dependence parameter. We first present an empirical analysis that motivates the theoretical analysis which follows. The theoretical analysis considers a simple two-state, first-order Markov chain model without covariates in which both transition probabilities are heterogeneous. Using such a model we are able to derive exact small sample results for bias and mean squared…

固定效应动态面板离散选择模型的估计AI 解读

In this paper, I consider the estimation of dynamic binary choice panel data models with fixed effects. I use a Modified Maximum Likelihood Estimator (MMLE) that reduces the order of the bias in the Maximum Likelihood Estimator from O(T^(-1)) to O(T^(-2)), without increasing the asymptotic variance. I evaluate its performance in finite samples where T is not large, using Monte Carlo simulations. In Probit and Logit models containing lags of the endogenous variable and…