000			01842nam a22002297a 4500
003			ZW-GwMSU
005			20240402125634.0
008			240402b |||||||| |||| 00| 0 eng d
040			_aMSU _bEnglish _cMSU _erda
050	1		_aHB139T.52 ECO
100	1		_aHillier, Grant H _eauthor
245	1	7	_aOn the conditional likelihood ratio test for several parameters in iv regression _cby Grant Hillier
264	1		_aCambridge : _bCambridge University Press, _c2009
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_aEconometric theory _vVolume 25, number 2
520			_aFor the problem of testing the hypothesis that all m coefficients of the right-hand-side endogenous variables in an instrumental variables (IV) regression are zero, the likelihood ratio (LR) test can, if the reduced form covariance matrix is known, be rendered similar by a conditioning argument. To exploit this fact requires knowledge of the relevant conditional cumulative distribution function (c.d.f.) of the LR statistic, but the statistic is a function of the smallest characteristic root of an (m + 1)-square matrix and is therefore analytically difficult to deal with when m > 1. We show in this paper that an iterative conditioning argument used by Hillier (2009) and Andrews, Moreira, and Stock (2007 Journal of Econometrics 139, 116–132) to evaluate the c.d.f. in the case m = 1 can be generalized to the case of arbitrary m. This means that we can completely bypass the difficulty of dealing with the smallest characteristic root. Analytic results are obtained for the case m = 2, and a simple and efficient simulation approach to evaluating the c.d.f. is suggested for larger values of m.
650			_aEstimation theory
856			_u10.1017/S0266466608090105
942			_2lcc _cJA
999			_c164609 _d164609