Covariate Measurement Error: Bias Reduction under Response-Based Sampling
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Esmeralda A. Ramalho
In this paper we propose a general framework to deal with the presence of covariate measurement error (CME) in response-based (RB) samples. Using Chesher's (1991) methodology, we obtain a small error variance approximation for the contaminated sampling distributions that characterize RB samples with CME. Then, following Chesher (2000), we develop the generalized method of moments (GMM) estimators that reduce the bias of the most well known likelihood-based estimators for RB samples which ignore the existence of CME and derive a score test to detect the presence of this type of measurement error. Our approach only requires the specification of the conditional distribution of the response variable given the latent covariates and the classical additive measurement error model assumption, the availability of information on both the marginal probability of the strata in the population and the variance of the measurement error not being essential. Monte Carlo evidence is presented which suggests that, in RB samples of moderate sizes, the bias-reduced GMM estimators perform well.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Artikel in diesem Heft
- Article
- Skew-Normal Mixture and Markov-Switching GARCH Processes
- Covariate Measurement Error: Bias Reduction under Response-Based Sampling
- Detection of Stationarity in Nonlinear Processes: A Comparison between Structural Breaks and Three-Regime TAR Models
- Fundamental and Behavioural Drivers of Electricity Price Volatility
- Bayesian Estimation and Model Selection in the Generalized Stochastic Unit Root Model
- A Nonlinear Algorithm for Seasonal Adjustment in Multiplicative Component Decompositions
