A Comparison of Some Bayesian and Classical Procedures for Simultaneous Equation Models with Weak Instruments
Chuanming Gao () and
Kajal Lahiri ()
Additional contact information
Chuanming Gao: Fannie Mae, 1100 15th St NW, Washington, DC 20005, USA
Econometrics, 2019, vol. 7, issue 3, 1-28
We compare the finite sample performance of a number of Bayesian and classical procedures for limited information simultaneous equations models with weak instruments by a Monte Carlo study. We consider Bayesian approaches developed by Chao and Phillips, Geweke, Kleibergen and van Dijk, and Zellner. Amongst the sampling theory methods, OLS, 2SLS, LIML, Fuller’s modified LIML, and the jackknife instrumental variable estimator (JIVE) due to Angrist et al. and Blomquist and Dahlberg are also considered. Since the posterior densities and their conditionals in Chao and Phillips and Kleibergen and van Dijk are nonstandard, we use a novel “Gibbs within Metropolis–Hastings” algorithm, which only requires the availability of the conditional densities from the candidate generating density. Our results show that with very weak instruments, there is no single estimator that is superior to others in all cases. When endogeneity is weak, Zellner’s MELO does the best. When the endogeneity is not weak and ρ ω 12 > 0 , where ρ is the correlation coefficient between the structural and reduced form errors, and ω 12 is the covariance between the unrestricted reduced form errors, the Bayesian method of moments (BMOM) outperforms all other estimators by a wide margin. When the endogeneity is not weak and β ρ < 0 ( β being the structural parameter), the Kleibergen and van Dijk approach seems to work very well. Surprisingly, the performance of JIVE was disappointing in all our experiments.
Keywords: limited information estimation; weak instruments; Metropolis–Hastings algorithm; Gibbs sampler; Monte Carlo method (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
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:gam:jecnmx:v:7:y:2019:i:3:p:33-:d:252767
Access Statistics for this article
Econometrics is currently edited by Prof. Dr. Kerry Patterson
More articles in Econometrics from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().