Abstract

http://ssrn.com/abstract=1269722
 
 

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


Hrishikesh D. Vinod


Fordham University - Department of Economics

September 17, 2008

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

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.

Number of Pages in PDF File: 43

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

JEL Classification: C13, C22

working papers series


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Date posted: September 19, 2008  

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: http://ssrn.com/abstract=1269722 or http://dx.doi.org/10.2139/ssrn.1269722

Contact Information

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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