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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…

利用合成残差估计跨期配置参数AI 解读

We present a novel structural estimation procedure for models of intertemporal allocation. This is based on modelling expectations errors directly; we refer to it as synthetic residual estimation (SRE). The flexibility of SRE allows us to account for measurement error in consumption and for heterogeneity in intertemporal allocation parameters. An investigation of the small sample properties of the SRE estimator indicates that it dominates generalized method of moments (GMM)…

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…

含噪声数据下的欧拉方程估计：两种精确 GMM 估计量AI 解读

Abstract In this paper we exploit the specific structure of the Euler equation and develop two alternative GMM estimators that deal explicitly with measurement error. The first estimator assumes that the measurement error is log‐normally distributed. The second estimator drops the distributional assumption at the cost of less precision. Our Monte Carlo results suggest that both proposed estimators perform much better than conventional alternatives based on the exact Euler…

非参数恩格尔曲线与显示偏好AI 解读

This paper applies revealed preference theory to the nonparametric statistical analysis of\nconsumer demand. Knowledge of expansion paths is shown to improve the power of nonparametric\ntests of revealed preference. The tightest bounds on indifference surfaces and\nwelfare measures are derived using an algorithm for which revealed preference conditions\nare shown to guarantee convergence. Nonparametric Engel curves are used to estimate\nexpansion paths and provide a…