Micro-Econometrics: Methods of Moments and Limited Dependent VariablesWhenIwrotethebookMethodsofMomentsandSemiparametricEco- metrics for Limited Dependent Variable Models published from Springer in 1996, my motivation was clear: there was no book available to convey the latest messages in micro-econometrics. The messages were that most eco- metric estimators can be viewed as method-of-moment estimators and that inferences for models with limited dependent variables (LDV) can be done without going fully parametric. Time has passed and there are now several books available for the same purpose. These days, methods of moments are the mainstay in econometrics, not just in micro-, but also in macro-econometrics. Many papers have been published for semiparametric methods and LDV models. I, myself, learned much over the years since 1996, so much so that my own view on what should be taught, and how, has changed much. Particularly, my exposure to the “sample selection” and “treatment e?ect” literature has changed the way I look at econometrics now. When I set out to write the second edition of the 1996 book, these changes prompted me to re-title, reorganize, and re-focus the book. |
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Contents
1 | |
Chapter 2 Methods of Moments for Multiple Linear Equation Systems | 53 |
Chapter 3 MEstimator and Maximum Likelihood Estimator MLE | 91 |
Chapter 4 Nonlinear Models and Estimators | 133 |
Chapter 5 Parametric Methods for Single Equation LDV Models | 177 |
Chapter 6 Parametric Methods for Multiple Equation LDV Models | 228 |
Chapter 7 Kernel Nonparametric Estimation | 303 |
Chapter 8 BandwidthFree Semiparametric Methods | 363 |
Other editions - View all
Micro-Econometrics: Methods of Moments and Limited Dependent Variables Myoung-jae Lee No preview available - 2014 |
Micro-Econometrics: Methods of Moments and Limited Dependent Variables Myoung-jae Lee No preview available - 2008 |
Micro-Econometrics: Methods of Moments and Limited Dependent Variables Myoung-jae Lee No preview available - 2009 |
Common terms and phrases
analogous assumption asymptotic distribution asymptotic variance bandwidth bias binary response blse bootstrap censored confidence Consider consistent estimator constant continuous convergence Define denote depends derivative dummy duration econometrics effect empirical endogenous endogenous regressors equation error term example find finite first first-order condition first-stage fixed hazard Hence heteroskedasticity holds homoskedasticity identified indicator function instance integral kernel estimator LDV models likelihood likelihood function linear model log-likelihood log-likelihood function logit M-estimator matrix maximizing mean median method minimand minimizing Newey nonlinear nonparametric estimator nonparametric regression observed obtained omitted variable bias panel data probability probit problem pseudo quantile regression random Recall regression function regressors replaced restriction rv’s sample satisfies semiparametric shows smooth specification subsection Suppose test statistic transformation truncated two-stage variables vector Wald test Weibull zero