<p>高级计量必备书,pdf格式，共135页</p><p>WHITNEY K. NEWEY和DANIEL McFADDEN合著的LARGE SAMPLE ESTIMATION AND HYPOTHESIS TESTING</p><p></p><p> </p><p><br/></p><p>LARGE SAMPLE ESTIMATION AND HYPOTHESIS<br/>TESTING*<br/>WHITNEY K. NEWEY<br/>Massachusetts Institute of Technology<br/>DANIEL McFADDEN<br/>University of California. Berkeley<br/>Contents<br/>Abstract<br/>1. Introduction<br/>2. Consistency<br/>2.1. The basic consistency theorem<br/>2.2. Identification<br/>2.2.1. The maximum likelihood estimator<br/>2.2.2. Nonlinear least squares<br/>2.2.3. Generalized method of moments<br/>2.2.4. Classical minimum distance<br/>2.3. Uniform convergence and continuity<br/>2.4. Consistency of maximum likelihood<br/>2.5. Consistency of GMM<br/>2.6. Consistency without compactness<br/>2.7. Stochastic equicontinuity and uniform convergence<br/>2.8. Least aosolute deviations examples<br/>2.8.1. Maximum score<br/>2.8.2. Censored least absolute deviations<br/>3. Asymptotic normality<br/>3.1. The basic results<br/>3.2. Asymptotic normality for MLE<br/>3.3. Asymptotic normality for GMM<br/>3.4. One-step theorems<br/>3.5. Technicalities<br/>4. Consistent asymptotic variance estimation<br/>4.1. The basic results<br/>4.2. Variance estimation for MLE<br/>4.3. Asymptotic variance estimation for GMM<br/>5. Asymptotic efficiency<br/>5.1. Efficiency of maximum likelihood estimation<br/>5.2. Optimal minimum distance estimation<br/>5.3. A general efficiency framework<br/>5.4. Solving for the smallest asymptotic variance<br/>5.5. Feasible efficient estimation<br/>5.6. Technicalities<br/>6. Two-step estimators<br/>6.1. Two-step estimators as joint GMM estimators<br/>6.2. The effect of first-step estimation on second-step standard errors<br/>6.3. Consistent asymptotic variance estimation for two-step estimators<br/>7. Asymptotic normality with nonsmooth objective functions<br/>7.1. The basic results<br/>7.2. Stochastic equicontinuity for Lipschitz moment functions<br/>7.3. Asymptotic variance estimation<br/>7.4. Technicalities<br/>8. Semi parametric two-step estimators<br/>8.1. Asymptotic normality and consistent variance estimation<br/>8.2. V -estimators<br/>8.3. First-step kernel estimation<br/>8.4. Technicalities<br/>9. Hypothesis testing with GMM estimators<br/>9.1. The null hypothesis and the constrained GMM estimator<br/>9.2. The test statistics<br/>9.3. One-step versions of the trinity<br/>9.4. Special cases<br/>9.5. Tests for overidentifying restrictions<br/>9.6. Specification tests in linear models<br/>9.7. Specification testing in multinomial models<br/>9.8. Technicalities<br/>References</p><p></p>

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