Large sample estimation and hypothesis testing
Whitney Newey and
Chapter 36 in Handbook of Econometrics, 1986, vol. 4, pp 2111-2245 from Elsevier
Asymptotic distribution theory is the primary method used to examine the properties of econometric estimators and tests. We present conditions for obtaining cosistency and asymptotic normality of a very general class of estimators (extremum estimators). Consistent asymptotic variance estimators are given to enable approximation of the asymptotic distribution. Asymptotic efficiency is another desirable property then considered. Throughout the chapter, the general results are also specialized to common econometric estimators (e.g. MLE and GMM), and in specific examples we work through the conditions for the various results in detail. The results are also extended to two-step estimators (with finite-dimensional parameter estimation in the first step), estimators derived from nonsmooth objective functions, and semiparametric two-step estimators (with nonparametric estimation of an infinite-dimensional parameter in the first step). Finally, the trinity of test statistics is considered within the quite general setting of GMM estimation, and numerous examples are given.
JEL-codes: C39 (search for similar items in EconPapers)
References: Add references at CitEc
Citations: View citations in EconPapers (143) Track citations by RSS feed
Downloads: (external link)
http://www.sciencedirect.com/science/article/B7GX7 ... fddfbd9c845eb4174cf5
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecochp:4-36
Access Statistics for this chapter
More chapters in Handbook of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().