 IV, GMM or likelihood approach to estimate dynamic panel models when either N or T or both are largeCheng Hsiao and Junwei Zhang Journal of Econometrics, 2015, vol. 187, issue 1, 312-322 Abstract: We examine the asymptotic properties of IV, GMM or MLE to estimate dynamic panel data models when either NorT or both are large. We show that the Anderson and Hsiao (1981, 1982) simple instrumental variable estimator (IV) or maximizing the likelihood function with initial value distribution properly treated (quasi-maximum likelihood estimator) is asymptotically unbiased when either N or T or both tend to infinity. On the other hand, the QMLE mistreating the initial value as fixed is asymptotically unbiased only if N is fixed and T is large. If both N and T are large and NT→c (c≠0,c<∞) as T→∞, it is asymptotically biased of order NT. We also explore the source of the bias of the Arellano and Bond (1991) type GMM estimator. We show that it is asymptotically biased of order TN if TN→c (c≠0,c<∞) as N→∞ even if we restrict the number of instruments used. Monte Carlo studies show that whether an estimator is asymptotically biased or not has important implications on the actual size of the conventional t-test. Keywords: IV; MLE; GMM; Asymptotic bias; Large N, T (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:187:y:2015:i:1:p:312-322 DOI: 10.1016/j.jeconom.2015.01.008 Access Statistics for this article Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson More articles in Journal of Econometrics from Elsevier |
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