Working Papers Series
Papers below are in pdf.
Douglas J. Miller
WP 07-18
Behavioral Foundations for Conditional Markov Models of Aggregate Data
Douglas J. Miller
Conditional Markov chain models of observed aggregate share–type data have been
used by economic researchers for several years, but the classes of models commonly
used in practice are often criticized as being purely ad hoc because they are not derived
from micro–behavioral foundations. The primary purpose of this paper is to show that
the estimating equations commonly used to estimate these conditional Markov chain
models may be derived from the assumed statistical properties of an agent–specific discrete
decision process. Thus, any conditional Markov chain model estimated from these
estimating equations may be compatible with some underlying agent–specific decision
process. The secondary purpose of this paper is to use an information theoretic approach
to derive a new class of conditional Markov chain models from this set of estimating
equations. The proposed modeling framework is based on the behavioral foundations
but does not require specific assumptions about the utility function or other components
of the agent–specific discrete decision process. The asymptotic properties of the
proposed estimators are developed to facilitate model selection procedures and classical
tests of behavioral hypotheses.
JEL Codes: C40, C51
Keywords: controlled stochastic process, Fréchet derivative, first–order Markov chain,
Cressie–Read power divergence criterion
WP 07-17
An Information Theoretic Approach
to Flexible Stochastic Frontier Models
Douglas J. Miller
Parametric stochastic frontier models have a long history in applied production eco- nomics, but the class of tractible parametric models is relatively small. Consequently, researchers have recently considered non–parametric alternatives such as kernel den- sity estimators, functional approximations, and data envelopment analysis (DEA). The purpose of this paper is to present an information theoretic approach to constructing more flexible classes of parametric stochastic frontier models. Further, the proposed class of models nests all of the commonly used parametric methods as special cases, and the proposed modeling framework provides a comprehensive means to conduct model specification tests. The modeling framework is also extended to develop information theoretic measures of mean technical efficiency and to construct a profile likelihood estimator of the stochastic frontier model.
JEL Codes: C13, C21, C51
Keywords: Kullback–Leibler information criterion, output distance function, profile
likelihood, stochastic frontier, technical efficiency