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Problems with the Asymptotic Theory of Maximum Likelihood Estimation in Integrated and Cointegrated Systems

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  • Saikkonen, Pentti

Abstract

Problems with the asymptotic theory of nonlinear maximum likelihood estimation in integrated and cointegrated systems are discussed in this paper. One problem is that standard proofs of consistency generally do not apply; another one is that, even if the consistency has been established, it can be difficult to deduce the limiting distribution of a maximum likelihood estimator from a conventional Taylor series expansion of the score vector. It is argued in this paper that the latter difficulty can generally be resolved if, in addition to consistency, an appropriate result of the order of consistency of the long-run parameter estimator of the model is available and the standardized sample information matrix satisfies a suitable extension of previous stochastic equicontinuity conditions. To make this idea applicable in particular cases, extensions of the author's recent stochastic equicontinuity results, relevant for many integrated and cointegrated systems with nonlinearities in parameters, are provided. As an illustration, a simple regression model with integrated and stationary regressors and nonlinearities in parameters is considered. In this model, the consistency and order of consistency of the long-run parameter estimator are obtained by employing extensions of well-known sufficient conditions for consistency. These conditions are applicable quite generally, and their verification in the special case of this paper suggests how to proceed in more complex models.

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  • Saikkonen, Pentti, 1995. "Problems with the Asymptotic Theory of Maximum Likelihood Estimation in Integrated and Cointegrated Systems," Econometric Theory, Cambridge University Press, vol. 11(05), pages 888-911, October.
  • Handle: RePEc:cup:etheor:v:11:y:1995:i:05:p:888-911_00
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    Cited by:

    1. Kristensen, Dennis & Rahbek, Anders, 2010. "Likelihood-based inference for cointegration with nonlinear error-correction," Journal of Econometrics, Elsevier, vol. 158(1), pages 78-94, September.
    2. Boswijk, H. Peter & Cavaliere, Giuseppe & Rahbek, Anders & Taylor, A.M. Robert, 2016. "Inference on co-integration parameters in heteroskedastic vector autoregressions," Journal of Econometrics, Elsevier, vol. 192(1), pages 64-85.
    3. Park, Joon Y & Phillips, Peter C B, 2001. "Nonlinear Regressions with Integrated Time Series," Econometrica, Econometric Society, vol. 69(1), pages 117-161, January.
    4. Myung Hwan Seo, 2007. "Estimation of Nonlinear Error CorrectionModels," STICERD - Econometrics Paper Series 517, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    5. M. Hashem Pesaran & Yongcheol Shin, 2002. "Long-Run Structural Modelling," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 49-87.
    6. de Jong, Robert M., 2002. "Nonlinear minimization estimators in the presence of cointegrating relations," Journal of Econometrics, Elsevier, vol. 110(2), pages 241-259, October.
    7. Yongcheol Shin & Ron P Smith & Mohammad Hashem Pesaran, 1998. "Pooled Mean Group Estimation of Dynamic Heterogeneous Panels," ESE Discussion Papers 16, Edinburgh School of Economics, University of Edinburgh.
    8. Chambers, M.J. & McCrorie, J.R., 2004. "Frequency Domain Gaussian Estimation of Temporally Aggregated Cointegrated Systems," Discussion Paper 2004-40, Tilburg University, Center for Economic Research.
    9. Phillips, Peter C.B. & Li, Degui & Gao, Jiti, 2017. "Estimating smooth structural change in cointegration models," Journal of Econometrics, Elsevier, vol. 196(1), pages 180-195.
    10. Dietmar Bauer & Martin Wagner, 2002. "Asymptotic Properties of Pseudo Maximum Likelihood Estimates for Multiple Frequency I(1) Processes," Diskussionsschriften dp0205, Universitaet Bern, Departement Volkswirtschaft.
    11. D. S. Poskitt, 2004. "On The Identification and Estimation of Partially Nonstationary ARMAX Systems," Monash Econometrics and Business Statistics Working Papers 20/04, Monash University, Department of Econometrics and Business Statistics.
    12. H. Peter Boswijk & Jurgen A. Doornik, 2004. "Identifying, estimating and testing restricted cointegrated systems: An overview," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(4), pages 440-465.
    13. Kristensen, Dennis & Shin, Yongseok, 2012. "Estimation of dynamic models with nonparametric simulated maximum likelihood," Journal of Econometrics, Elsevier, vol. 167(1), pages 76-94.
    14. Nedeljkovic, Milan, 2008. "Testing for Smooth Transition Nonlinearity in Adjustments of Cointegrating Systems," The Warwick Economics Research Paper Series (TWERPS) 876, University of Warwick, Department of Economics.
    15. Dennis Kristensen & Anders Rahbek, 2007. "Likelihood-Based Inference in Nonlinear Error-Correction Models," CREATES Research Papers 2007-38, Department of Economics and Business Economics, Aarhus University.
    16. Kunpeng Li & Degui Li & Zhongwen Lian & Cheng Hsiao, 2013. "Semiparametric Profile Likelihood Estimation of Varying Coefficient Models with Nonstationary Regressors," Monash Econometrics and Business Statistics Working Papers 2/13, Monash University, Department of Econometrics and Business Statistics.
    17. Chambers, Marcus J. & Roderick McCrorie, J., 2007. "Frequency domain estimation of temporally aggregated Gaussian cointegrated systems," Journal of Econometrics, Elsevier, vol. 136(1), pages 1-29, January.
    18. Robert M. deJong, 2000. "Nonlinear Minimization Estimators in the Presence of Cointegrating Relations," Econometric Society World Congress 2000 Contributed Papers 1651, Econometric Society.
    19. Seo, Byeongseon, 1999. "Distribution theory for unit root tests with conditional heteroskedasticity1," Journal of Econometrics, Elsevier, vol. 91(1), pages 113-144, July.
    20. Hernández Juan R., 2016. "Unit Root Testing in ARMA Models: A Likelihood Ratio Approach," Working Papers 2016-03, Banco de México.

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