Econometric analysis of policy effect
Abstract/Contents
- Abstract
- The generalized method of moments (GMM) is a general approach to tackle problems of endogeneity which is very common in econometric studies. However, the asymptotic theory of GMM provides a poor approximation to finite sample performance of its estimators and associated test statistics largely due to the weak or a large number of moment conditions. The focus of this dissertation is to propose a new approach for moment restriction models that has advantages over previous approaches when some moment restrictions are weakly informative or when a particular subvector rather than the whole parameter vector is of interest. Our method consists of two stages and is denoted by 2SMMS (two-stage model and moment selection). First, we would like to eliminate from consideration those models that do not approximate well the true model. Second, among those models and moment restrictions which are not eliminated, we would like to choose the model and moment restriction combination which provides the best estimate of the parameter vector of interest. Theoretical analysis shows that under regularity conditions our procedure chooses the correct model that has the largest number of moment restrictions and the smallest number of unknown parameters, whose covariance matrix is the smallest according to some prescribed measures. Our new procedure is also illustrated in a simulation study and an empirical study, which show that ours improve significantly upon Andrews and Lu's (2001).
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Copyright date | 2011 |
| Publication date | 2010, c2011; 2010 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Liu, Jia |
|---|---|
| Associated with | Stanford University, Department of Management Science and Engineering |
| Primary advisor | Lai, T. L |
| Thesis advisor | Lai, T. L |
| Thesis advisor | Giesecke, Kay |
| Thesis advisor | Weyant, John P. (John Peter) |
| Advisor | Giesecke, Kay |
| Advisor | Weyant, John P. (John Peter) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Jia Liu. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2011. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2011 by Jia Liu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial No Derivatives 3.0 Unported license (CC BY-NC-ND).
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