## Abstract Many research questions, such as quality of specific providers or guidelineβconcordant care in typical practices, are commonly studied in an observational setting. These analyses face the risk that covariates related to both an outcome of interest and the probability of treatment are un
Estimating Optimal Weights for Instrumental Variable Methods
β Scribed by Petre Stoica; Magnus Jansson
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 187 KB
- Volume
- 11
- Category
- Article
- ISSN
- 1051-2004
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β¦ Synopsis
The accuracy of the instrumental variable approach to parameter estimation (also called correlation analysis) can be significantly improved by optimally weighting the estimating equations. However, the optimal weight in general depends on unknown quantities and hence must be itself estimated before its use becomes possible. The optimal weighting matrix encountered in a typical parameter estimation problem is equal to the DC power of a certain vector sequence and usually it has to be consistently estimated in a nonparametric manner. Consistent nonparametric estimation of power (at any frequency, zero or not) is not an easy task and typically it cannot be achieved by using "natural" estimators (such as the periodogram). Surprisingly, it turns out that in the case considered in this paper there is a natural consistent estimator of the DC power matrix that represents the desired optimal weight. This result will promote the use of optimally weighted parameter estimation methods of instrumental variable or related types; it may also have some interesting consequences for nonparametric spectral analysis which though are not explored herein.
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## Abstract This paper presents a method which aims at improving parameter estimation in dynamical systems. The general principle of the method is based on a modification of the leastβsquares objective function by means of a weighting operator, in view to improve the conditioning of the identificat