Computationally Intensive Strategies for Structural Modelling

Project: Research project

Project Details

Description

This proposal requests continued support for a program of research in nonlinear econometric methods. The motivation for this line of research is that econometric inference regarding the ideas, theories, and models from economics should be made without compromise to the discipline: Economic models should not be simplified or crudely approximated to accommodate limits imposed by econometric theory.

Research under the previous grant focused on estimation of parametric and semiparametric structural models that are so complex that they can only be estimated by simulation methods. The idea was to require that moments determined by simulation from the structural model match the scores of a certain truncation sieve. This estimator is as efficient as maximum likelihood would be were it feasible and is termed efficient method of moments or EMM for that reason. Diagnostics based on the scores that can pinpoint the reasons for model failure were developed. These methods work well when data is abundant such as financial econometrics. When applied in areas where data is sparse, such as macroeconomics, the maximum permissible truncation point becomes so small that the diagnostics cannot detect a model's inability to track important features of the data such as conditional scale and a claim that passing the diagnostic tests certifies a model's adequacy becomes suspect.

This award supports the development of methods similar to EMM that work well when data is sparse. The most promising is a Bayesian approach for which the structural model is taken as the prior on a truncation sieve. The advantage is that the prior amounts to a dimensionality reduction that makes computations feasible when data is sparse. Relaxation of the prior leads to informative diagnostics. The Markov chain Monte Carlo computational strategy developed for this Bayesian estimator can also be applied to frequentist estimators. Because it is more robust than traditional quasi Newton hill climbing methods, estimation using nonstandard criterion functions becomes practicable. The Cramer-Von Mises criterion is an instance that is interesting because it is defensible when the economic model is admittedly misspecified and it gives results that seem qualitatively similar to the Bayesian approach in applications. A theoretical justification of Cramer-Von Mises estimates is sought. This project also continues an ongoing program of empirical work that exploits new methodologies developed under this program of econometric research. Initial work will focus on determining whether structural macro models that can pass tests of model adequacy based on macro time series data will continue to do so when required to confront, in addition, data on cash flows from a cross section of assets.

Broad impact: The availability of practicable methods to determine from data the parameters of substantive models rich enough to accurately represent target phenomena and the availability of methods to assess the adequacy of such models is of considerable societal importance. The methods developed within the program have been applied in science, business, and government. In thesciences diffusion has been broad; a sample of citations in non-economic and non-statistical journals is the following: American Journal of Epidemiology, American Naturalist, Annals of Human Biology, Ecology, Journal of Pharmacokinetics and Biopharmaceutics, Journal of Psychiatric Research, Nature, Physica D, and Physics and Chemistry of the Earth C. Diffusion to business and government has been primarily through inclusion of methods developed within the project in commercial statistical packages such as SAS.

StatusFinished
Effective start/end date7/1/056/30/08

Funding

  • National Science Foundation: $247,062.00

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