IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v48y2007i3p357-383.html

Maximal invariant likelihood based testing of semi-linear models

Author

Listed:
  • Jahar Bhowmik
  • Maxwell King

Abstract

In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) tests. The first involves testing for the inclusion of a non-linear regressor and the second involves testing of a linear regressor against the alternative of a non-linear regressor. We report the results of a Monte Carlo experiment that compares the size and power properties of the traditional LR tests with those of our proposed MIL based LR tests. Our simulation results show that in both cases the MIL based tests have more accurate asymptotic critical values and better behaved (i.e., better centred) power curves than their classical counterparts
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Jahar Bhowmik & Maxwell King, 2007. "Maximal invariant likelihood based testing of semi-linear models," Statistical Papers, Springer, vol. 48(3), pages 357-383, September.
    • Handle: RePEc:spr:stpapr:v:48:y:2007:i:3:p:357-383
    DOI: 10.1007/s00362-006-0342-7
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00362-006-0342-7
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00362-006-0342-7?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 look for a different version below or

    for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. McManus, Douglas A. & Nankervis, John C. & Savin, N. E., 1994. "Multiple optima and asymptotic approximations in the partial adjustment model," Journal of Econometrics, Elsevier, vol. 62(2), pages 91-128, June.
    2. Martin, Vance L., 1998. "Econometric Society Australasian Meetings 1997 (ESAM97)," Econometric Theory, Cambridge University Press, vol. 14(06), pages 800-801, December.
    3. Rahman, S. & King, M.L., 1994. "A Comparison of Marginal Likelihood Based and Approximate Point Optimal Tests for Random Regression Coefficient in the Presence of Autocorrelation," Monash Econometrics and Business Statistics Working Papers 4/94, Monash University, Department of Econometrics and Business Statistics.
    4. Laskar, Mizan R. & King, Maxwell L., 1997. "Modified Wald test for regression disturbances," Economics Letters, Elsevier, vol. 56(1), pages 5-11, September.
    5. Rahman, Shahidur & King, Maxwell L., 1997. "Marginal-likelihood score-based tests of regression disturbances in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 82(1), pages 81-106.
    6. Konstas, Panos & Khouja, Mohamad W, 1969. "The Keynesian Demand-for-Money Function: Another Look and Some Additional Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 1(4), pages 765-777, November.
    7. Laskar, M.R. & King, M.L., 1998. "Modified Likelihood and Related Methods for Handling Nuisance Parameters in the Linear Regression Model," Monash Econometrics and Business Statistics Working Papers 5/98, Monash University, Department of Econometrics and Business Statistics.
    8. Moulton, Brent R & Randolph, William C, 1989. "Alternative Tests of the Error Components Model," Econometrica, Econometric Society, vol. 57(3), pages 685-693, May.
    9. Ara, I. & King, M.L., 1995. "Marginal Likelihood Based Tests of a Subvector of the Parameter Vector of Linear Regression Disturbances," Monash Econometrics and Business Statistics Working Papers 12/95, Monash University, Department of Econometrics and Business Statistics.
    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. Jahar L. Bhowmik & Maxwell L. King, 2005. "Parameter Estimation in Semi-Linear Models Using a Maximal Invariant Likelihood Function," Monash Econometrics and Business Statistics Working Papers 18/05, Monash University, Department of Econometrics and Business Statistics.
    2. Badi Baltagi & Seuck Heun Song & Byoung Cheol Jung, 2002. "Simple Lm Tests For The Unbalanced Nested Error Component Regression Model," Econometric Reviews, Taylor & Francis Journals, vol. 21(2), pages 167-187.
    3. Sriananthakumar, Sivagowry, 2013. "Testing linear regression model with AR(1) errors against a first-order dynamic linear regression model with white noise errors: A point optimal testing approach," Economic Modelling, Elsevier, vol. 33(C), pages 126-136.
    4. Rahman, Shahidur & King, Maxwell L., 1997. "Marginal-likelihood score-based tests of regression disturbances in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 82(1), pages 81-106.
    5. Jean-Bernard Chatelain & Kirsten Ralf, 2017. "Can We Identify the Fed's Preferences?," Working Papers halshs-01549908, HAL.
    6. Andrea Vaona & Mario Pianta, 2008. "Firm Size and Innovation in European Manufacturing," Small Business Economics, Springer, vol. 30(3), pages 283-299, March.
    7. Chimere O. Iheonu, 2019. "Governance and Domestic Investment in Africa," Working Papers 19/001, European Xtramile Centre of African Studies (EXCAS).
    8. Koen Jochmans, 2020. "Testing for correlation in error‐component models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 860-878, November.
    9. Juan Huang & Poomthan Rangkakulnuwat, 2024. "Does years of schooling matter for economic growth at different development levels? New evidence from China," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 75, pages 5-32.
    10. Ahmet Hamdi Yanık & Ahmet İncekara, 2024. "Relationship Between Household Consumption and Disaggregated Wealth Components in OECD Countries: Panel Data Analysis for the Period 2010-2017," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 11(2), pages 370-382, July.
    11. Blanchard, Pierre & Matyas, Laszlo, 1996. "Robustness of tests for error components models to non-normality," Economics Letters, Elsevier, vol. 51(2), pages 161-167, May.
    12. Neil Kellard & Denise Osborn & Jerry Coakley & Nathan E. (Gene) Savin, 2015. "Papers with John," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 663-671, September.
    13. Dufour, Jean-Marie & Khalaf, Lynda & Kichian, Maral, 2006. "Inflation dynamics and the New Keynesian Phillips Curve: An identification robust econometric analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1707-1727.
    14. Glenn D. Rudebusch, 2006. "Monetary Policy Inertia: Fact or Fiction?," International Journal of Central Banking, International Journal of Central Banking, vol. 2(4), December.
    15. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    16. Nazan An & Mustafa Tufan Turp & Murat Türkeş & Mehmet Levent Kurnaz, 2020. "Mid-Term Impact of Climate Change on Hazelnut Yield," Agriculture, MDPI, vol. 10(5), pages 1-20, May.
    17. Wu, Jianhong & Qin, Jinxu & Ding, Qing, 2015. "A moment-based test for individual effects in the error component model with incomplete panels," Statistics & Probability Letters, Elsevier, vol. 104(C), pages 153-162.
    18. Kapteyn, Arie & Alessie, Rob & Lusardi, Annamaria, 2005. "Explaining the wealth holdings of different cohorts: Productivity growth and Social Security," European Economic Review, Elsevier, vol. 49(5), pages 1361-1391, July.
    19. Jae Kim & Mahbuba Yeasmin, 2005. "The Size and Power of the Bias-Corrected Bootstrap Test for Regression Models with Autocorrelated Errors," Computational Economics, Springer;Society for Computational Economics, vol. 25(3), pages 255-267, June.
    20. Rudebusch, Glenn D., 2002. "Term structure evidence on interest rate smoothing and monetary policy inertia," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1161-1187, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

    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:spr:stpapr:v:48:y:2007:i:3:p:357-383. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.