Titlebook: Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models; Myoung-jae Lee Book 19961st edition Springer Sci

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BOOR

https://doi.org/10.1007/978-1-4757-2550-6Micro-econometrics; econometrics; limited dependent variables; methods of moments; nonparametrics; semipa

鈍劍

Least Squares and Method of MomentsIn a linear model .with .(.) = 0, where . is a . × 1 parameter vector of interest, . is the error term, . is a . × 1 regressor vector, and .are iid, the least squares estimator (LSE) for . is obtained by minimizing . with respect to (wrt) .. LSE can also be viewed as the solution of the first-order (moment) condition of the minimization

Lethargic

Extremum Estimators and Method-of-Moments EstimatorsLSE and IVE are rare cases where the estimators are written in closed forms. Often estimators are defined implicitly by .where . is a parameter space and ..and we often omit . in .(., .).

Curmudgeon

Maximum Likelihood EstimationLet . be an iid sample drawn from a known distribution .(..,.., .), where ? is a . × 1 vector of unknown parameters. Let ..(., .) denote the . of . | ., which is the density function of . | . if . |. is continuous or the probability of . | . if . | . is discrete. Define ..(.) analogously, which is not a function of ..

沒(méi)有準(zhǔn)備

Nonlinear Models and Generalized Method of MomentsConsider a nonlinear regression model .where . is a . × 1 vector and the form of .(·) is known. In contrast to the linear model, the dimension of . is not necessarily the same as that of .. Depending on cases, we may omit either . or . in .(.). A model more general than (1.1) is .which includes (1.1) as a special case when .(., .) = y ? .(.).

性行為放縱者

賠償

thwart

Nonparametric Regressionn .(.∣.) ≡ .(.), where . = .(.) + . and .(.∣.) = 0. More generally, we can consider functionals of the conditional distribution ...., such as ?.(.)/?. and .(.∣.). But usually, estimation methods for the functionals can be inferred from those for .(.∣.).

Intrepid

Book 19961st editioninstrumental variable estimation, nonparametric density and regression function estimation and semiparametric limited dependent variable models, the book provides details of how these methods may be implemented using software.