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Heteroscedasticity and Autocorrelation Efficient (HAE) Estimation and Pivots for Jointly Evolving Series

Fordham University Department of Economics Discussion Paper No. 2008-15

43 Pages Posted: 19 Sep 2008  

Hrishikesh D. Vinod

Fordham University - Department of Economics

Date Written: September 17, 2008

Abstract

A new two-way map between time domain and numerical magnitudes or values domain (v-dom) provides a new solution to heteroscedasticity. Since sorted logs of squared fitted residuals are monotonic in the v-dom, we obtain a parsimonious fit there. Two theorems prove consistency, asymptotic normality, efficiency and specification-robustness, supplemented by a simulation. Since Dufour's (1997) impossibility theorems show how confidence intervals from Wald-type tests can have zero coverage, I suggest Godambe pivot functions (GPF) with good finite sample coverage and distribution-free robustness. I use the Frisch-Waugh theorem and the scalar GPF to construct new confidence intervals for regression parameters and apply Vinod's (2004, 2006) maximum entropy bootstrap. I use Irving Fisher's model for interest rates and Keynesian consumption function for illustration.

Keywords: regression, bootstrap, simulation, Fisher equation, Permanent income hypothesis

JEL Classification: C13, C22

Suggested Citation

Vinod, Hrishikesh D., Heteroscedasticity and Autocorrelation Efficient (HAE) Estimation and Pivots for Jointly Evolving Series (September 17, 2008). Fordham University Department of Economics Discussion Paper No. 2008-15. Available at SSRN: https://ssrn.com/abstract=1269722 or http://dx.doi.org/10.2139/ssrn.1269722

Hrishikesh D. Vinod (Contact Author)

Fordham University - Department of Economics ( email )

Dealy Hall
Bronx, NY 10458
United States
718-817-4065 (Phone)
718-817-3518 (Fax)

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