计量经济分析（第六版）答案 Notes-22.doc

Econometrics I Professor William Greene Notes 22. Two Step ML and GMM Estimation I. Two step estimation A. Setting, fitting a model which contains parameter estimates from another model. B. Typical application, inserting a prediction from one model into another. C. Procedures: How its done. D. Asymptotic results: 1. Consistency 2. Getting an appropriate estimator of the asymptotic covariance matrix 3. The Murphy - Topel result (Next page) E. Application: Equation 1: Number of children Equation 2: Labor force participation II. GMM estimation A. Moment Equations Examples: 1. From familiar estimation settings: a. Least squares b. Instrumental variables c. Maximum likelihood estimation 2. From behavioral models: E[(ct - some expectation) | some set of variables in an information set] = 0 What does this imply? B. The Method of Moments. Solving the moment equations 1. Exactly identified cases 2. Overidentified cases C. Consistency. How do we know the method of moments is consistent? D. Asymptotic covariance matrix. E. Consistent vs. Efficient estimation 1. A weighting matrix 2. The minimum distance estimator 3. What is the efficient weighting matrix? 4. Estimating the weighting matrix. F. The Generalized method of moments estimator - how it is computed. G. Computing the appropriate asymptotic covariance matrix III. Testing Hypotheses in the GMM framework. A. Testing hypotheses about the parameters: 1. Wald Test 2. A counterpart to the likelihood ratio test B. Testing the overidentifying restrictions IV. Application (Monday) ? Data transformations. Number of kids, scale income variables ? Create ; Kids = kl6 + k618 ; income = faminc/10000 ; Wifeinc = ww*whrs/1000 $ ? ? Equation 1, number of kids. Standard Poisson fertility model. ? Fit equation, collect parameters Theta1 and covariance matrix V1 ? compute fitted values. ? Namelist; Z = one,wa,we,income,wif