IDEAS home Printed from https://ideas.repec.org/p/mlb/wpaper/1079.html
   My bibliography  Save this paper

Notes on the Construction of Geometric Representations of Confidence Intervals of Ratios using Stata, Gauss and Eviews

Author

Abstract

These notes demonstrate how one can define optimization problems whose solutions can be interpreted as the Delta and the Fieller confidence intervals for a ratio of normally distributed parameter estimates. Also included in these notes are the details of the derivation of the slope of a constraint ellipse that is common to both optimizations. In addition, these notes provide an example of how one might generate a graphic representation of both optimization problems using the Stata, Gauss and Eviews statistical computer programs.

Suggested Citation

  • Joe Hirschberg & Jenny Lye, 2009. "Notes on the Construction of Geometric Representations of Confidence Intervals of Ratios using Stata, Gauss and Eviews," Department of Economics - Working Papers Series 1079, The University of Melbourne.
    • Handle: RePEc:mlb:wpaper:1079
    as

    Download full text from publisher

    File URL: http://fbe.unimelb.edu.au/__data/assets/pdf_file/0007/801169/1079.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hirschberg, Joe & Lye, Jenny, 2010. "A Geometric Comparison of the Delta and Fieller Confidence Intervals," The American Statistician, American Statistical Association, vol. 64(3), pages 234-241.
    2. Ruud, Paul A., 2000. "An Introduction to Classical Econometric Theory," OUP Catalogue, Oxford University Press, number 9780195111644.
    3. Anders Alexandersson, 2004. "Graphing confidence ellipses: An update of ellip for Stata 8," Stata Journal, StataCorp LP, vol. 4(3), pages 242-256, September.
    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. Carson, Richard T. & Czajkowski, Mikołaj, 2019. "A new baseline model for estimating willingness to pay from discrete choice models," Journal of Environmental Economics and Management, Elsevier, vol. 95(C), pages 57-61.
    2. Jan R. Magnus & Wendun Wang & Xinyu Zhang, 2016. "Weighted-Average Least Squares Prediction," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1040-1074, June.
    3. Nathan H. Miller, 2008. "Competition When Consumers Value Firm Scope," EAG Discussions Papers 200807, Department of Justice, Antitrust Division.
    4. Doko Tchatoka, Firmin Sabro, 2012. "Specification Tests with Weak and Invalid Instruments," MPRA Paper 40185, University Library of Munich, Germany.
    5. John M. Abowd & Francis Kramarz & Sébastien Pérez-Duarte & Ian M. Schmutte, 2018. "Sorting Between and Within Industries: A Testable Model of Assortative Matching," Annals of Economics and Statistics, GENES, issue 129, pages 1-32.
    6. Lullit Getachew & Robin C. Sickles, 2007. "The policy environment and relative price efficiency of Egyptian private sector manufacturing: 1987|88-1995|96," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 703-728.
    7. Hugo Benítez-Silva & Debra Dwyer & Wayne-Roy Gayle & Thomas Muench, 2008. "Expectations in micro data: rationality revisited," Empirical Economics, Springer, vol. 34(2), pages 381-416, March.
    8. Antonio Ciccone & Giovanni Peri, 2005. "Long-Run Substitutability Between More and Less Educated Workers: Evidence from U.S. States, 1950-1990," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 652-663, November.
    9. Gianmarco I.P. Ottaviano & Giovanni Peri, 2005. "Rethinking the Gains from Immigration: Theory and Evidence from the U.S," NBER Working Papers 11672, National Bureau of Economic Research, Inc.
    10. Rose, Heather, 2006. "Do gains in test scores explain labor market outcomes?," Economics of Education Review, Elsevier, vol. 25(4), pages 430-446, August.
    11. Florian Heiss, 2016. "Discrete Choice Methods with Simulation," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 688-692, April.
    12. Jakusch, Sven Thorsten, 2017. "On the applicability of maximum likelihood methods: From experimental to financial data," SAFE Working Paper Series 148, Leibniz Institute for Financial Research SAFE, revised 2017.
    13. Benitez-Silva, Hugo & Dwyer, Debra S., 2006. "Expectation formation of older married couples and the rational expectations hypothesis," Labour Economics, Elsevier, vol. 13(2), pages 191-218, April.
    14. Doko Tchatoka, Firmin & Dufour, Jean-Marie, 2020. "Exogeneity tests, incomplete models, weak identification and non-Gaussian distributions: Invariance and finite-sample distributional theory," Journal of Econometrics, Elsevier, vol. 218(2), pages 390-418.
    15. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    16. Woutersen, Tiemen & Hausman, Jerry A., 2019. "Increasing the power of specification tests," Journal of Econometrics, Elsevier, vol. 211(1), pages 166-175.
    17. Hugo Benítez-Silva & Debra S. Dwyer, 2003. "What to Expect when you are Expecting Rationality: Testing Rational Expectations using Micro Data," Working Papers wp037, University of Michigan, Michigan Retirement Research Center.
    18. Jeffrey M. Wooldridge, 2001. "Applications of Generalized Method of Moments Estimation," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 87-100, Fall.
    19. repec:tiu:tiucen:200457 is not listed on IDEAS
    20. Joe Hirschberg & Jenny Lye, 2017. "Alternative Graphical Representations of the Confidence Intervals for the Structural Coefficient from Exactly Identified Two-Stage Least Squares," Department of Economics - Working Papers Series 2026, The University of Melbourne.
    21. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2003. "Instrumental variables and GMM: Estimation and testing," Stata Journal, StataCorp LP, vol. 3(1), pages 1-31, March.

    More about this item

    Keywords

    Fieller method; Delta method; marginal ellipse;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other

    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:mlb:wpaper:1079. 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: Dandapani Lokanathan (email available below). General contact details of provider: https://edirc.repec.org/data/demelau.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.