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Advances in Info-MetricsInformation and Information Processing across Disciplines$
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Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah

Print publication date: 2020

Print ISBN-13: 9780190636685

Published to Oxford Scholarship Online: December 2020

DOI: 10.1093/oso/9780190636685.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 17 June 2021

Information-Theoretic Estimation of Econometric Functions

Information-Theoretic Estimation of Econometric Functions

Chapter:
(p.507) 19 Information-Theoretic Estimation of Econometric Functions
Source:
Advances in Info-Metrics
Author(s):

Millie Yi Mao

Aman Ullah

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190636685.003.0019

This chapter introduces an information-theoretic approach to specify econometric functions as an alternative to avoid parametric assumptions. We investigate the performances of the information-theoretic method in estimating the regression (conditional mean) and response (derivative) functions. We have demonstrated that they are easy to implement and are advantageous over parametric models and nonparametric kernel techniques. For the implementation of our estimation method, a recursive integration procedure is introduced. An empirical illustration is also presented to study the Canadian earning function. The asymptotic properties of the regression and derivative functions are established. In addition, a test for normality is also proposed.

Keywords:   information theory, maximum entropy distributions, econometric functions, conditional mean, estimation, derivative

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