IDEAS home Printed from https://ideas.repec.org/p/frd/wpaper/dp2008-15.html
   My bibliography  Save this paper

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

Author

Listed:
  • Hrishikesh D. Vinod

    (Fordham University, Department of Economics)

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.

Suggested Citation

  • Hrishikesh D. Vinod, 2008. "Heteroscedasticity and Autocorrelation Efficient (HAE) Estimation and Pivots for Jointly Evolving Series," Fordham Economics Discussion Paper Series dp2008-15, Fordham University, Department of Economics.
    • Handle: RePEc:frd:wpaper:dp2008-15
    as

    Download full text from publisher

    File URL: https://archive.fordham.edu/ECONOMICS_RESEARCH/PAPERS/dp2008_15_vinod.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cribari-Neto, Francisco, 2004. "Asymptotic inference under heteroskedasticity of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 215-233, March.
    2. Robinson, P M, 1987. "Asymptotically Efficient Estimation in the Presence of Heteroskedasticity of Unknown Form," Econometrica, Econometric Society, vol. 55(4), pages 875-891, July.
    3. Peter C. B. Phillips, 2005. "Econometric Analysis of Fisher's Equation," American Journal of Economics and Sociology, Wiley Blackwell, vol. 64(1), pages 125-168, January.
    4. Vinod, H. D., 1998. "FELLOW'S CORNER Foundations of statistical inference based on numerical roots of robust pivot functions," Journal of Econometrics, Elsevier, vol. 86(2), pages 387-396, June.
    5. Antonio E. Noriega & Daniel Ventosa‐Santaulària, 2006. "Spurious Regression Under Broken‐Trend Stationarity," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 671-684, September.
    6. Vinod, H. D., 2004. "Ranking mutual funds using unconventional utility theory and stochastic dominance," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 353-377, June.
    7. Cragg, John G, 1983. "More Efficient Estimation in the Presence of Heteroscedasticity of Unknown Form," Econometrica, Econometric Society, vol. 51(3), pages 751-763, May.
    8. Godfrey, L.G., 2006. "Tests for regression models with heteroskedasticity of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2715-2733, June.
    9. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    10. B. D. McCullough & H. D. Vinod, 2003. "Verifying the Solution from a Nonlinear Solver: A Case Study," American Economic Review, American Economic Association, vol. 93(3), pages 873-892, June.
    11. Spanos,Aris, 1999. "Probability Theory and Statistical Inference," Cambridge Books, Cambridge University Press, number 9780521424080.
    12. Vinod, H. D., 1985. "Exact maximum likelihood regression estimation with ARMA (n, n - 1) errors," Economics Letters, Elsevier, vol. 17(4), pages 355-358.
    13. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    14. Jean-Marie Dufour, 1997. "Some Impossibility Theorems in Econometrics with Applications to Structural and Dynamic Models," Econometrica, Econometric Society, vol. 65(6), pages 1365-1388, November.
    15. Vinod, Hrishikesh D., 2006. "Maximum entropy ensembles for time series inference in economics," Journal of Asian Economics, Elsevier, vol. 17(6), pages 955-978, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pötscher, Benedikt M. & Preinerstorfer, David, 2021. "Valid Heteroskedasticity Robust Testing," MPRA Paper 107420, University Library of Munich, Germany.
    2. Pötscher, Benedikt M. & Preinerstorfer, David, 2023. "How Reliable Are Bootstrap-Based Heteroskedasticity Robust Tests?," Econometric Theory, Cambridge University Press, vol. 39(4), pages 789-847, August.
    3. Eric S. Lin & Ta-Sheng Chou, 2018. "Finite-sample refinement of GMM approach to nonlinear models under heteroskedasticity of unknown form," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 1-28, January.
    4. Katarzyna Jabłońska, 2018. "Dealing With Heteroskedasticity Within The Modeling Of The Quality Of Life Of Older People," Statistics in Transition New Series, Polish Statistical Association, vol. 19(3), pages 423-452, September.
    5. Zeileis, Achim, 2006. "Implementing a class of structural change tests: An econometric computing approach," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 2987-3008, July.
    6. Romano, Joseph P. & Wolf, Michael, 2017. "Resurrecting weighted least squares," Journal of Econometrics, Elsevier, vol. 197(1), pages 1-19.
    7. Michael O'Hara & Christopher F. Parmeter, 2013. "Nonparametric Generalized Least Squares in Applied Regression Analysis," Pacific Economic Review, Wiley Blackwell, vol. 18(4), pages 456-474, October.
    8. Rohin Anhal, 2013. "Causality between GDP, Energy and Coal Consumption in India, 1970-2011: A Non-parametric Bootstrap Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 3(4), pages 434-446.
    9. Hartigan, Luke, 2018. "Alternative HAC covariance matrix estimators with improved finite sample properties," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 55-73.
    10. Pellini, Elisabetta, 2021. "Estimating income and price elasticities of residential electricity demand with Autometrics," Energy Economics, Elsevier, vol. 101(C).
    11. Hrishikesh D. Vinod & Surendra K. Kaushik, 2007. "Human Capital and Economic Growth: Evidence from Developing Countries," The American Economist, Sage Publications, vol. 51(1), pages 29-39, March.
    12. Preinerstorfer, David & Pötscher, Benedikt M., 2016. "On Size And Power Of Heteroskedasticity And Autocorrelation Robust Tests," Econometric Theory, Cambridge University Press, vol. 32(2), pages 261-358, April.
    13. Rand R. Wilcox, 2017. "Linear regression: robust heteroscedastic confidence bands that have some specified simultaneous probability coverage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(14), pages 2564-2574, October.
    14. H.D. Vinod, 2016. "New bootstrap inference for spurious regression problems," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(2), pages 317-335, February.
    15. Hiroaki Chigira & Tsunemasa Shiba, 2012. "Dirichlet Prior for Estimating Unknown Regression Error Heteroscedasticity," Global COE Hi-Stat Discussion Paper Series gd12-248, Institute of Economic Research, Hitotsubashi University.
    16. So Im, Kyung, 1998. "Efficient estimation with grouped data," Economics Letters, Elsevier, vol. 59(2), pages 169-174, May.
    17. José Curto & José Pinto & Ana Morais & Isabel Lourenço, 2011. "The heteroskedasticity-consistent covariance estimator in accounting," Review of Quantitative Finance and Accounting, Springer, vol. 37(4), pages 427-449, November.
    18. Jabłońska Katarzyna, 2018. "Dealing With Heteroskedasticity Within The Modeling Of The Quality Of Life Of Older People," Statistics in Transition New Series, Statistics Poland, vol. 19(3), pages 433-452, September.
    19. Maria Heracleous & Andreas Koutris & Aris Spanos, 2006. "Testing for Structural Breaks and other forms of Non-stationarity: a Misspecification Perspective," Computing in Economics and Finance 2006 493, Society for Computational Economics.
    20. Kleibergen, F., 1996. "Reduced Rank of Regression Using Generalized Method of Moments Estimators," Other publications TiSEM 5caf1c0c-d988-4184-acf7-d, Tilburg University, School of Economics and Management.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:frd:wpaper:dp2008-15. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Fordham Economics (email available below). General contact details of provider: https://edirc.repec.org/data/edforus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.