A Semiparametric Estimator for Dynamic Optimization Models
Han Hong and
Matthew Shum ()
Economics Working Paper Archive from The Johns Hopkins University,Department of Economics
We develop a new estimation methodology for dynamic optimization models with unobserved state variables Our approach is semiparametric in the sense of not requiring explicit parametric assumptions to be made concerning the distribution of these unobserved state variables We propose a two-step pairwise-difference estimator which exploits two common features of dynamic optimization problems: (1) the weak monotonicity of the agent's decision (policy) function in the unobserved state variables conditional on the observed state variables; and (2) the state-contingent nature of optimal decision-making which implies that conditional on the observed state variables the variation in observed choices across agents must be due to randomness in the unobserved state variables across agents We apply our estimator to a model of dynamic competitive equilibrium in the market for milk production quota in Ontario Canada
Date: 2000-05, Revised 2001-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Our link check indicates that this URL is bad, the error code is: 404 Not Found (http://econ.jhu.edu/wp-content/uploads/pdf/papers/double.pdf [301 Moved Permanently]--> http://krieger2.jhu.edu/economics/wp-content/uploads/pdf/papers/double.pdf [301 Moved Permanently]--> https://krieger2.jhu.edu/economics/wp-content/uploads/pdf/papers/double.pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:jhu:papers:461
Access Statistics for this paper
More papers in Economics Working Paper Archive from The Johns Hopkins University,Department of Economics 3400 North Charles Street Baltimore, MD 21218. Contact information at EDIRC.
Bibliographic data for series maintained by None (). This e-mail address is bad, please contact .