The method of simulated scores for the estimation of LDV models

• 成果类型: Article
• 署名作者: Hajivassiliou, VA; McFadden, DL
• 署名单位: University of London; London School Economics & Political Science; University of California System; University of California Berkeley
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.2307/2999576
• 发表日期: 1998
• 页码: 863-896
• 关键词: NORMAL RECTANGLE PROBABILITIES; Multinomial probit model; distributions; derivatives; algorithm; moments

摘要

The method of simulated scores (MSS) is presented for estimating limited dependent variables models (LDV) with flexible correlation structure in the unobservables. We propose simulators that are continuous in the unknown parameter vectors, and hence standard optimization methods can be used to compute the MSS estimators that employ these simulators. The first continuous method relies on a recursive conditioning of the multivariate normal density through a Cholesky triangularization of its variance-covariance matrix. The second method combines results about the conditionals of the multivariate normal distribution with Gibbs resampling techniques. We establish consistency and asymptotic normality of the MSS estimators and derive suitable rates at which the number of simulations must rise if biased simulators are used.