IDEAS home Printed from https://ideas.repec.org/p/may/mayecw/n1620106.pdf.html
   My bibliography  Save this paper

Irrelevant but highly persistent instruments in stationary regressions with endogenous variables containing near-to-unit roots

Author

Listed:
  • Ekaterini Panopoulou

    (Economics, National University of Ireland, Maynooth)

  • Nicolaos Kourogenis
  • Nikitas Pittis

Abstract

This paper suggests that IV estimators, utilizing irrelevant but persistent instruments mai produce reliable inferences, in small samples, in cases where the endogenous variables contaii autoregressive roots near unity. In such cases, these estimators appear to outperform IV estimator: with strong instruments as well as some asymptotically efficient cointegration estimators.

Suggested Citation

  • Ekaterini Panopoulou & Nicolaos Kourogenis & Nikitas Pittis, 2006. "Irrelevant but highly persistent instruments in stationary regressions with endogenous variables containing near-to-unit roots," Economics Department Working Paper Series n1620106.pdf, Department of Economics, National University of Ireland - Maynooth.
    • Handle: RePEc:may:mayecw:n1620106.pdf
    as

    Download full text from publisher

    File URL: http://repec.maynoothuniversity.ie/mayecw-files/N1620106.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peter C. B. Phillips & Bruce E. Hansen, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 57(1), pages 99-125.
    2. Peter C. B. Phillips & Mico Loretan, 1991. "Estimating Long-run Economic Equilibria," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(3), pages 407-436.
    3. Bekker, Paul A, 1994. "Alternative Approximations to the Distributions of Instrumental Variable Estimators," Econometrica, Econometric Society, vol. 62(3), pages 657-681, May.
    4. Park, Joon Y. & Phillips, Peter C.B., 1988. "Statistical Inference in Regressions with Integrated Processes: Part 1," Econometric Theory, Cambridge University Press, vol. 4(3), pages 468-497, December.
    5. Buse, A, 1992. "The Bias of Instrumental Variable Estimators," Econometrica, Econometric Society, vol. 60(1), pages 173-180, January.
    6. 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.
    7. Jiahui Wang & Eric Zivot, 1998. "Inference on Structural Parameters in Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 66(6), pages 1389-1404, November.
    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. Lightwood, James & Glantz, Stanton A., 2011. "Predicted Effect of California Tobacco Control Funding on Smoking Prevalence, Cigarette Consumption, and Healthcare Costs, 2012-2016," University of California at San Francisco, Center for Tobacco Control Research and Education qt6j03515p, Center for Tobacco Control Research and Education, UC San Francisco.

    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. Chao, John & Swanson, Norman R., 2007. "Alternative approximations of the bias and MSE of the IV estimator under weak identification with an application to bias correction," Journal of Econometrics, Elsevier, vol. 137(2), pages 515-555, April.
    2. Ekaterini Panopoulou, 2005. "A Resolution of the Fisher Effect Puzzle: A Comparison of Estimators," Money Macro and Finance (MMF) Research Group Conference 2005 18, Money Macro and Finance Research Group.
    3. DUFOUR, Jean-Marie & JASIAK, Joanna, 1998. "Finite-Sample Inference Methods for Simultaneous Equations and Models with Unobserved and Generated Regressors," Cahiers de recherche 9812, Universite de Montreal, Departement de sciences economiques.
    4. Matsushita, Yukitoshi & Otsu, Taisuke, 2022. "A jackknife Lagrange multiplier test with many weak instruments," LSE Research Online Documents on Economics 116392, London School of Economics and Political Science, LSE Library.
    5. Utku Utkulu & Durmus Özdemir, 2005. "Does Trade Liberalization Cause a Long Run Economic Growth in Turkey," Economic Change and Restructuring, Springer, vol. 37(3), pages 245-266, September.
    6. Bekker, Paul A. & Lawford, Steve, 2008. "Symmetry-based inference in an instrumental variable setting," Journal of Econometrics, Elsevier, vol. 142(1), pages 28-49, January.
    7. Doko Tchatoka, Firmin Sabro & Dufour, Jean-Marie, 2008. "Instrument endogeneity and identification-robust tests: some analytical results," MPRA Paper 29613, University Library of Munich, Germany.
    8. Khalaf, Lynda & Urga, Giovanni, 2014. "Identification robust inference in cointegrating regressions," Journal of Econometrics, Elsevier, vol. 182(2), pages 385-396.
    9. Paul A. Bekker & Jan van der Ploeg, 2000. "Instrumental Variable Estimation Based on Grouped Data," Econometric Society World Congress 2000 Contributed Papers 1862, Econometric Society.
    10. Markus Frölich & Michael Lechner, 2004. "Regional treatment intensity as an instrument for the evaluation of labour market policies," University of St. Gallen Department of Economics working paper series 2004 2004-08, Department of Economics, University of St. Gallen.
    11. 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.
    12. Utku Utkulu & Dilek Seymen, 2004. "Trade and Competitiveness Between Turkey and the EU: Time Series Evidence," Working Papers 2004/8, Turkish Economic Association, revised Mar 2004.
    13. James L. Powell, 2017. "Identification and Asymptotic Approximations: Three Examples of Progress in Econometric Theory," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 107-124, Spring.
    14. D.S. Poskitt & C.L. Skeels, 2005. "Small Concentration Asymptotics and Instrumental Variables Inference," Department of Economics - Working Papers Series 948, The University of Melbourne.
    15. Phillips, Peter C B, 1995. "Fully Modified Least Squares and Vector Autoregression," Econometrica, Econometric Society, vol. 63(5), pages 1023-1078, September.
    16. Nelson C. Mark & Masao Ogaki & Donggyu Sul, 2005. "Dynamic Seemingly Unrelated Cointegrating Regressions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 797-820.
    17. Richard Startz & Charles Nelson & Eric Zivot, 1999. "Improved Inference for the Instrumental Variable Estimator," Working Papers 0039, University of Washington, Department of Economics.
    18. Dufour, Jean-Marie, 2001. "Logique et tests d’hypothèses," L'Actualité Economique, Société Canadienne de Science Economique, vol. 77(2), pages 171-190, juin.
    19. Guo, Zijian & Kang, Hyunseung & Cai, T. Tony & Small, Dylan S., 2018. "Testing endogeneity with high dimensional covariates," Journal of Econometrics, Elsevier, vol. 207(1), pages 175-187.
    20. Hasanov, Fakhri J. & Shannak, Sa'd, 2020. "Electricity incentives for agriculture in Saudi Arabia. Is that relevant to remove them?," Energy Policy, Elsevier, vol. 144(C).

    More about this item

    Keywords

    Instrumental variables estimator; persistent instruments; near-to-unit roots.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:may:mayecw:n1620106.pdf. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/demayie.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.