IDEAS home Printed from https://ideas.repec.org/p/fip/feddgw/92809.html
   My bibliography  Save this paper

Pooled Bewley Estimator of Long-Run Relationships in Dynamic Heterogenous Panels

Author

Abstract

Using a transformation of the autoregressive distributed lag model due to Bewley, a novel pooled Bewley (PB) estimator of long-run coefficients for dynamic panels with heterogeneous short-run dynamics is proposed. The PB estimator is directly comparable to the widely used Pooled Mean Group (PMG) estimator, and is shown to be consistent and asymptotically normal. Monte Carlo simulations show good small sample performance of PB compared to the existing estimators in the literature, namely PMG, panel dynamic OLS (PDOLS) and panel fully-modified OLS (FMOLS). Application of two bias-correction methods and a bootstrapping of critical values to conduct inference robust to cross-sectional dependence of errors are also considered. The utility of the PB estimator is illustrated in an empirical application to the aggregate consumption function.

Suggested Citation

  • Alexander Chudik & M. Hashem Pesaran & Ron P. Smith, 2021. "Pooled Bewley Estimator of Long-Run Relationships in Dynamic Heterogenous Panels," Globalization Institute Working Papers 409, Federal Reserve Bank of Dallas, revised 08 Nov 2023.
    • Handle: RePEc:fip:feddgw:92809
    DOI: 10.24149/gwp409r2
    as

    Download full text from publisher

    File URL: https://www.dallasfed.org/~/media/documents/research/international/wpapers/2021/0409r2.pdf
    File Function: Full text
    Download Restriction: no
    File URL: https://www.dallasfed.org/-/media/documents/research/international/wpapers/2021/04092.zip
    File Function: Codes
    Download Restriction: no
    File URL: https://libkey.io/10.24149/gwp409r2?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Alexander Chudik & M. Hashem Pesaran & Jui‐Chung Yang, 2018. "Half‐panel jackknife fixed‐effects estimation of linear panels with weakly exogenous regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(6), pages 816-836, September.
    2. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(3), pages 991-1030.
    3. Jorg Breitung, 2005. "A Parametric approach to the Estimation of Cointegration Vectors in Panel Data," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 151-173.
    4. repec:bla:obuest:v:61:y:1999:i:0:p:631-52 is not listed on IDEAS
    5. Alexander Chudik & M. Hashem Pesaran, 2013. "Econometric Analysis of High Dimensional VARs Featuring a Dominant Unit," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 592-649, August.
    6. Peter C. B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Econometrica, Econometric Society, vol. 67(5), pages 1057-1112, September.
    7. Davidson, Russell & Flachaire, Emmanuel, 2008. "The wild bootstrap, tamed at last," Journal of Econometrics, Elsevier, vol. 146(1), pages 162-169, September.
    8. Nelson C. Mark & Donggyu Sul, 2003. "Cointegration Vector Estimation by Panel DOLS and Long‐run Money Demand," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(5), pages 655-680, December.
    9. Peter Pedroni, 2001. "Purchasing Power Parity Tests In Cointegrated Panels," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 727-731, November.
    10. Alexander Chudik & M. Hashem Pesaran & Ron P. Smith, 2023. "Revisiting the Great Ratios Hypothesis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 1023-1047, October.
    11. Bewley, R. A., 1979. "The direct estimation of the equilibrium response in a linear dynamic model," Economics Letters, Elsevier, vol. 3(4), pages 357-361.
    12. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," Review of Economic Studies, Oxford University Press, vol. 82(3), pages 991-1030.
    13. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
    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. Breitung, Jörg & Pesaran, Mohammad Hashem, 2005. "Unit roots and cointegration in panels," Discussion Paper Series 1: Economic Studies 2005,42, Deutsche Bundesbank.
    2. Muhammad Zakaria & Bashir Ahmed Fida & Saquib Yousaf Janjua & Syed Jawad Hussain Shahzad, 2017. "Fertility and Financial Development in South Asia," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(2), pages 645-668, September.
    3. Herzer, Dierk & Nunnenkamp, Peter, 2015. "Income inequality and health: Evidence from developed and developing countries," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 9, pages 1-56.
    4. Carlsson, Mikael & Lyhagen, Johan & Österholm, Pär, 2007. "Testing for Purchasing Power Parity in Cointegrated Panels," Working Paper Series 2008:1, Uppsala University, Department of Economics.
    5. Acikgoz, Senay & Ben Ali, Mohamed Sami, 2019. "Where does economic growth in the Middle Eastern and North African countries come from?," The Quarterly Review of Economics and Finance, Elsevier, vol. 73(C), pages 172-183.
    6. VANNESTE, Jacques & ZHANG, Ying, 2012. "The impact of government expenditure on prepayment for health services: Evidence from cointegration analysis in heterogeneous panel data," Working Papers 2012029, University of Antwerp, Faculty of Business and Economics.
    7. Pedro Teles & Harald Uhlig & João Valle e Azevedo, 2016. "Is Quantity Theory Still Alive?," Economic Journal, Royal Economic Society, vol. 126(591), pages 442-464, March.
    8. In Choi, 2012. "Panel Cointegration," Working Papers 1208, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    9. João Valle e Azevedo & João Ritto & Pedro Teles, 2022. "The Neutrality Of Nominal Rates: How Long Is The Long Run?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1745-1777, November.
    10. Mr. Etienne B Yehoue & Gilles J. Dufrénot, 2005. "Real Exchange Rate Misalignment: A Panel Co-Integration and Common Factor Analysis," IMF Working Papers 2005/164, International Monetary Fund.
    11. Jäger, Philipp & Schmidt, Torsten, 2016. "The political economy of public investment when population is aging: A panel cointegration analysis," European Journal of Political Economy, Elsevier, vol. 43(C), pages 145-158.
    12. Wagner, Martin, 2008. "The carbon Kuznets curve: A cloudy picture emitted by bad econometrics?," Resource and Energy Economics, Elsevier, vol. 30(3), pages 388-408, August.
    13. Gabriel Bruneau & Kevin Moran, 2017. "Exchange rate fluctuations and labour market adjustments in Canadian manufacturing industries," Canadian Journal of Economics, Canadian Economics Association, vol. 50(1), pages 72-93, February.
    14. Ibrahima Amadou DIALLO, 2015. "Exchange Rate Volatility and Investment: A Panel Data Cointegration Approach," Expert Journal of Economics, Sprint Investify, vol. 3(2), pages 127-135.
    15. Cruz, Manuel David, 2023. "Labor Productivity, Real Wages, and Employment in OECD Economies," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 367-382.
    16. Fantazzini , Dean, 2009. "Econometric Analysis of Financial Data in Risk Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 14(2), pages 100-127.
    17. Martin Wagner & Jaroslava Hlouskova, 2010. "The Performance of Panel Cointegration Methods: Results from a Large Scale Simulation Study," Econometric Reviews, Taylor & Francis Journals, vol. 29(2), pages 182-223, April.
    18. Daniel Tomić Jurica Šimurina Luka Jovanov, 2020. "The Nexus between Economic Sentiment Indicator and Gross Domestic Product; a Panel Cointegration Analysis," Zagreb International Review of Economics and Business, Faculty of Economics and Business, University of Zagreb, vol. 23(1), pages 121-140, May.
    19. Nicole Grunewald & Inmaculada Martínez-Zarzoso, 2014. "Green Growth in Mexico, Brazil and Chile: Policy strategies and future prospects," Ibero America Institute for Econ. Research (IAI) Discussion Papers 229, Ibero-America Institute for Economic Research.
    20. Marã­A Paula Vargas & Erick Lahura, 2022. "Financial Development, Financial Inclusion And Informality: New International Evidence," Global Economy Journal (GEJ), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-42, September.

    More about this item

    Keywords

    Heterogeneous dynamic panels; I(1) regressors; pooled mean group estimator (PMG); Autoregressive-Distributed Lag model (ARDL); Bewley transform; PDOLS; FMOLS; bias correction; robust inference; cross-sectional dependence;
    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
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

    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:fip:feddgw:92809. 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: Amy Chapman (email available below). General contact details of provider: https://edirc.repec.org/data/frbdaus.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.