IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v39y2012i1p97-111.html
   My bibliography  Save this article

Discrepancy in regression estimates between log-normal and gamma: some case studies

Author

Listed:
  • Rabindra Nath Das
  • Jeong-Soo Park

Abstract

In regression models with multiplicative error, estimation is often based on either the log-normal or the gamma model. It is well known that the gamma model with constant coefficient of variation and the log-normal model with constant variance give almost the same analysis. This article focuses on the discrepancies of the regression estimates between the two models based on real examples. It identifies that even though the variance or the coefficient of variation remains constant, but regression estimates may be different between the two models. It also identifies that for the same positive data set, the variance is constant under the log-normal model but non-constant under the gamma model. For this data set, the regression estimates are completely different between the two models. In the process, it explains the causes of discrepancies between the two models.

Suggested Citation

  • Rabindra Nath Das & Jeong-Soo Park, 2012. "Discrepancy in regression estimates between log-normal and gamma: some case studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 97-111, March.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:97-111
    DOI: 10.1080/02664763.2011.578618
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2011.578618
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2011.578618?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. R. Scott Hacker & Abdulnasser Hatemi-J, 2008. "Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(6), pages 601-615.
    2. Youngjo Lee & John A. Nelder, 2006. "Double hierarchical generalized linear models (with discussion)," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 139-185, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rabindra Nath Das & Amar Nath Shit & Apurba Ratan Ghosh, 2015. "Carp Seed Production Factors in India," Journal of Environments, Asian Online Journal Publishing Group, vol. 2(1), pages 10-17.
    2. Rabindra Nath Das & Anis Chandra Mukhopadhyay, 2017. "Correlated random effects regression analysis for a log-normally distributed variable," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(5), pages 897-915, April.
    3. Sara Gustavsson & Björn Fagerberg & Gerd Sallsten & Eva M. Andersson, 2014. "Regression Models for Log-Normal Data: Comparing Different Methods for Quantifying the Association between Abdominal Adiposity and Biomarkers of Inflammation and Insulin Resistance," IJERPH, MDPI, vol. 11(4), pages 1-19, March.

    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. Yanyuan Ma & Marc G. Genton, 2010. "Explicit estimating equations for semiparametric generalized linear latent variable models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 475-495, September.
    2. Michaelides, Panayotis G. & Milios, John G. & Konstantakis, Konstantinos N. & Tarnaras, Panayiotis, 2015. "Quantity-of-money fluctuations and economic instability: empirical evidence for the USA (1958–2006)," MPRA Paper 90145, University Library of Munich, Germany.
    3. Leckie, George, 2014. "runmixregls: A Program to Run the MIXREGLS Mixed-Effects Location Scale Software from within Stata," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(c02).
    4. Michaelides, Panayotis G. & Papageorgiou, Theofanis, 2012. "On the transmission of economic fluctuations from the USA to EU-15 (1960–2011)," Journal of Economics and Business, Elsevier, vol. 64(6), pages 427-438.
    5. Peter McCullagh, 2008. "Sampling bias and logistic models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 643-677, September.
    6. I. Gijbels & I. Prosdocimi, 2011. "Smooth estimation of mean and dispersion function in extended generalized additive models with application to Italian induced abortion data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2391-2411, December.
    7. Ergin Akalpler, 2023. "Triggering economic growth to ensure financial stability: case study of Northern Cyprus," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-40, December.
    8. Lee, Woojoo & Lim, Johan & Lee, Youngjo & del Castillo, Joan, 2011. "The hierarchical-likelihood approach to autoregressive stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 248-260, January.
    9. Stephan B. Bruns & David I. Stern, 2019. "Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models," Empirical Economics, Springer, vol. 56(3), pages 797-830, March.
    10. Konstantinos N. Konstantakis & Theofanis Papageorgiou & Apostolos G. Christopoulos & Ioannis G. Dokas & Panayotis G. Michaelides, 2019. "Business cycles in Greek maritime transport: an econometric exploration (1998–2015)," Operational Research, Springer, vol. 19(4), pages 1059-1079, December.
    11. Wu, Jianmin & Bentler, Peter M., 2013. "Limited information estimation in binary factor analysis: A review and extension," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 392-403.
    12. Angeliki Skoura, 2019. "Detection of Lead-Lag Relationships Using Both Time Domain and Time-Frequency Domain; An Application to Wealth-To-Income Ratio," Economies, MDPI, vol. 7(2), pages 1-27, April.
    13. Yu, Dalei & Yau, Kelvin K.W., 2012. "Conditional Akaike information criterion for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 629-644.
    14. Mehmet Ivrendi & Douglas K. Pearce, 2014. "Asset prices and expected monetary policy: evidence from daily data," Applied Economics, Taylor & Francis Journals, vol. 46(9), pages 985-995, March.
    15. Kwon, Sunghoon & Oh, Seungyoung & Lee, Youngjo, 2016. "The use of random-effect models for high-dimensional variable selection problems," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 401-412.
    16. R. Scott Hacker & Abdulnasser Hatemi-J, 2021. "Model selection in time series analysis: using information criteria as an alternative to hypothesis testing," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 49(6), pages 1055-1075, September.
    17. Veli Yilanci & Onder Ozgur & Muhammed Sehid Gorus, 2021. "Stock prices and economic activity nexus in OECD countries: new evidence from an asymmetric panel Granger causality test in the frequency domain," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-22, December.
    18. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    19. Meza, Cristian & Jaffrézic, Florence & Foulley, Jean-Louis, 2009. "Estimation in the probit normal model for binary outcomes using the SAEM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1350-1360, February.
    20. Stephan B. Bruns, Christian Gross and David I. Stern, 2014. "Is There Really Granger Causality Between Energy Use and Output?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).

    More about this item

    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:taf:japsta:v:39:y:2012:i:1:p:97-111. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.