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Truncated dynamics and estimation of diffusion equations
截断动态与扩散方程估计
We study inference on continuous-time processes from discrete data with a given time interval between consecutive observations, and propose a modification of the sieve estimation method based on the infinitesimal generator. Our approach consists on truncating the initial process to improve the estimation of the eigenfunctions at the boundaries of the set of admissible values. For diffusion processes, nonparametric estimation of the drift and volatility are derived. A prior…
Statistical inference for poverty measures with relative poverty lines
Statistical inference for testing inequality indices with dependent samples
Nested random effects estimation in unbalanced panel data
GMM estimation of linear panel data models with time-varying individual effects
The memory of stochastic volatility models
The unbalanced nested error component regression model
Contemporaneous asymmetry in GARCH processes
A simultaneous estimation and variable selection rule
Nonlinear estimation using estimated cointegrating relations
An analysis of housing expenditure using semiparametric models and panel data
Causality tests and conditional heteroskedasticity:
Tests for the error component model in the presence of local misspecification
Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models
Robust inference with GMM estimators
GMM 估计量的稳健推断
The local robustness properties of generalized method of moments (GMM) estimators and of a broad class of GMM based tests are investigated in a unified framework. GMM statistics are shown to have bounded influence if and only if the function defining the orthogonality restrictions imposed on the underlying model is bounded. Since in many applications this function is unbounded, it is useful to have procedures that modify the starting orthogonality conditions in order to…
Two-stage rank estimation of quantile index models
Benchmark priors for Bayesian model averaging
贝叶斯模型平均的基准先验
In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, 'diffuse' priors on model-specific parameters can lead to quite unexpected consequences. Here we focus on the practically relevant situation where we need to entertain a (large) number of sampling models and we have (or wish to use) little or no subjective…