Exact Maximum Likelihood Estimation Of Observation Driven Econometric Models
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Exact Maximum Likelihood Estimation of Observation-driven Econometric Models
The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained. We conclude with a discussion of directions for future research, including application of our methods to panel data models.
Simulation-based Inference in Econometrics
Author: Roberto Mariano
language: en
Publisher: Cambridge University Press
Release Date: 2000-07-20
This substantial volume has two principal objectives. First it provides an overview of the statistical foundations of Simulation-based inference. This includes the summary and synthesis of the many concepts and results extant in the theoretical literature, the different classes of problems and estimators, the asymptotic properties of these estimators, as well as descriptions of the different simulators in use. Second, the volume provides empirical and operational examples of SBI methods. Often what is missing, even in existing applied papers, are operational issues. Which simulator works best for which problem and why? This volume will explicitly address the important numerical and computational issues in SBI which are not covered comprehensively in the existing literature. Examples of such issues are: comparisons with existing tractable methods, number of replications needed for robust results, choice of instruments, simulation noise and bias as well as efficiency loss in practice.
Observational Agency and Supply-side Econometrics
A central problem in applied empirical work is to separate out the patterns in the data that are due to poor production of the data, such as e.g. non-response and measurement errors, from the patterns attributable to the economic phenomena studied. This paper interprets this inference problem as being an agency problem in the market for observations and suggests ways in which using incentives may be useful to overcome it. The paper discusses how wage discrimination may be used for identification of economic parameters of interest taking into account the responses in survey supply by sample members to that discrimination. Random wage discrimination alters the supply behavior of sample members across the same types of populations in terms of outcomes and thereby allows for separating out poor supply from the population parameters of economic interest. Empirical evidence for a survey of US physicians suggests that survey supply even for this wealthy group is affected by the types of wage discrimination schemes discussed in a manner that makes the schemes useful for identification purposes. Using such schemes to correct mean estimates of physician earnings increases those earnings by about one third.