QML estimation of dynamic panel data mod
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QML estimation of dynamic panel data mod
OML Estimation of Dynamic Panel Data Models with SpatialErrors* *Lian&jun Stfand Zhenlin Yang*February. 2007AbstractWc propose quasi maximum likelihoo QML estimation of dynamic panel data mod od (QML) estimation of dynamic panel models with spatial errors when the cross-sectional dimension (n) is large and the lime dimension (Tl is fixed. We consider both the random effects and fixed effects models and derive the limiting distributions of the Q.\ll. estimators under different assumptions QML estimation of dynamic panel data mod on the individual effects and on the initial observations. Monte Carlo simulation shows that the estimators perform well in 1'milc samples.JEL classiQML estimation of dynamic panel data mod
fications: (?12. Cl I. C22. C5Key Words: Dynamic Panel. Fixed Effects, Random Effects. Spatial Dependence. Quasi Maximum Likelihood•T.iangjnn Sn grateOML Estimation of Dynamic Panel Data Models with SpatialErrors* *Lian&jun Stfand Zhenlin Yang*February. 2007AbstractWc propose quasi maximum likelihoo QML estimation of dynamic panel data mod University (SMU), for tile hospitality during lũi two month visit, and tile tvhartou SMC research center, SMU, fur supporting his visit, ztienlin Yang gratefully acknowledges tire research support from rhe VVharton-SMU research center, Singapore Management University.f Giinnghiia School of Manageme QML estimation of dynamic panel data mod nt. Peking University. Beijing 100S71, China. Telephone: -I £<ỉ-10-6271~7444. Email address: lsuUgsni.pku.cdu.cn.•School of Economics and Social ScienQML estimation of dynamic panel data mod
ces. Singapore Management University, 90 Stamford Road. Singapore 178908. Telephone: +€5-0828 0852. Fax: +€5-6828- 0858. Email address: xlyangỉlsmu.edOML Estimation of Dynamic Panel Data Models with SpatialErrors* *Lian&jun Stfand Zhenlin Yang*February. 2007AbstractWc propose quasi maximum likelihoo QML estimation of dynamic panel data mod altagi and Lt (2004). Baltagi. Song and Koh (2003). Chen and Conley (2001). Elhorst (2003. 2005). Huang (2004). Kapoor. Kelejian and Prucha (2006). Persaran (2003. 2004). Phillips and Sul (2003). Yang. Li and Tse (2006). among others, fur an overview, hl this paper we focus on the quasi-maximum like QML estimation of dynamic panel data mod lihood estimation of dynamic panel data models with spatial errors.The history’ of spatial econometrics can be traced back at least to Cliff and Ord (QML estimation of dynamic panel data mod
1973). Since then, various methods have been proposed to estimate the spatial dependence models, including the method of maximum likelihood (ML) (Ord.OML Estimation of Dynamic Panel Data Models with SpatialErrors* *Lian&jun Stfand Zhenlin Yang*February. 2007AbstractWc propose quasi maximum likelihoo QML estimation of dynamic panel data mod m likelihood (Q.ML) (Lee. 2001). A common feature of these methods is that they are all developed for estimations of estimate a cross sectional model with no time dimension. Recently. Elhorsl (2003. 2005) studies the ML estimation of (dynamic) panel data models with certain spatial dependence struct QML estimation of dynamic panel data mod ure, but the asymptotic properties of the estimators arc not given.for over thirty years of spatial econometrics history, the asymptotic theory for thQML estimation of dynamic panel data mod
e (Q)MI. esti mation of spatial models has been taken for granted until the influential paper by Lee (200 I), which establishes systematically the desOML Estimation of Dynamic Panel Data Models with SpatialErrors* *Lian&jun Stfand Zhenlin Yang*February. 2007AbstractWc propose quasi maximum likelihoo QML estimation of dynamic panel data mod convergence of the QMI. estimates may depend on some general features of the spatial weights matrix. More recently. Yu. de Jong, and Lee (2006) extend the work of Lee (200 I) to spatial dynamic panel data models with fixed effects allowing both the lime dimension (T) and the cross-sectional dimensi QML estimation of dynamic panel data mod on (n) large.This paper concerns with the more traditional panel data model where n is allowed ro grow hut 7’ is held fixed (usually small). As BinderQML estimation of dynamic panel data mod
. Hsiao and Eesaran (2005) remarked, this model remains the prevalent setting for the majority of empirical microcconomclric research. Our work is disOML Estimation of Dynamic Panel Data Models with SpatialErrors* *Lian&jun Stfand Zhenlin Yang*February. 2007AbstractWc propose quasi maximum likelihoo QML estimation of dynamic panel data mod shall consider hath random and fixed effects specification of the individual effects and highlight their differences and implications these differences have tor estimation and inference. Second, when we keep T fixed, our estimation strategy is quite different from that in the large-n and large-T se QML estimation of dynamic panel data mod tting. In case of fixed effects model, vve have to difference-outthe fixed effects whereas Yu, de Jong, and Lee (2006) need nor do so. Third, spatialQML estimation of dynamic panel data mod
dependence is present only in the error term in our model whereas Yu. de Jong, and Lee (2006) considers spatial lag model. Consequently the two approaOML Estimation of Dynamic Panel Data Models with SpatialErrors* *Lian&jun Stfand Zhenlin Yang*February. 2007AbstractWc propose quasi maximum likelihoo QML estimation of dynamic panel data mod e paper is organized as follows. In Section 2 we introduce our model specification. We propose the quasi maximum likelihood estimates in Section 3 and study their asymptotic properties in Section 4. In Section 5 we provide a small set of Monte Carlo experiments to evaluate the finite sample performa QML estimation of dynamic panel data mod nce of our estimators. All proofs arc relegated to the appendix.To proceed, we introduce some notation and convention. Let /„ denote an n X n identityQML estimation of dynamic panel data mod
matrix. Let I-T denote a 7’ X 1 vector of ones and ./j- = Ij-Gr. where prime denotes transposition throughout this paper. 0 denotes the Kronccker proOML Estimation of Dynamic Panel Data Models with SpatialErrors* *Lian&jun Stfand Zhenlin Yang*February. 2007AbstractWc propose quasi maximum likelihoo QML estimation of dynamic panel data mod 1for Ỉ = 1....n. t = I,..../. where the scalar parameter p with \p\ < 1 characterizes the dynamiceffect. ,rjt is a p X 1 vector of time varying exogenous variables, ij is a q X 1 vector of time invariant exogenous variables such as the constant term or dummies representing individuals’ gender or rac QML estimation of dynamic panel data mod e, and the disturbance vector «1 (uu, ...<«««) is assumed to exhibit both non-obscrvablc individual effects and spatially autocorrelated structure, i.QML estimation of dynamic panel data mod
e..ut =+t?t.-2.2-2.3where fl = ị/ỉỵ.../1„ I . £r = (sit..£nt | . and vt = (ỉiịe..I.'nt | . with fl representing the unobserv-able individual effects wOML Estimation of Dynamic Panel Data Models with SpatialErrors* *Lian&jun Stfand Zhenlin Yang*February. 2007AbstractWc propose quasi maximum likelihoo QML estimation of dynamic panel data mod independent and identically distributed (i.i.d.) with zero mean and variance o.<7^. In the case where fl is random, its elements are assumed to be i.i.d. (0. <7^) and to be independent of Vf. In the case where fl is fixed, the time invariant regressors should be removed from the model due to multico QML estimation of dynamic panel data mod llinearity between the3observed and unobserved individual-specific effects. The parameter A is a the spatial autoregressive coefficient and ir„ is a kQML estimation of dynamic panel data mod
nown n X. n spatial weight matrix whose diagonal elements are zero. Following the literature in spatial econometrics, we assume that /n — Airn is nonsOML Estimation of Dynamic Panel Data Models with SpatialErrors* *Lian&jun Stfand Zhenlin Yang*February. 2007AbstractWc propose quasi maximum likelihoo QML estimation of dynamic panel data mod l suppress the dependence of B,. and H’n ontf and writeBand II instead. We have .-.t = B~'vt. Let \jt = (ylf t/ntF- and xt = (xM.xntY• Define>= (y'l..................yý)'- y~l = (vó 07-1/» -Y = (*i xr)’ ■ and z = lT® 5. wherez=(si..zn)'. QML estimation of dynamic panel data mod OML Estimation of Dynamic Panel Data Models with SpatialErrors* *Lian&jun Stfand Zhenlin Yang*February. 2007AbstractWc propose quasi maximum likelihooGọi ngay
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