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Estimation and Testing for Fractional Cointegration

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  • Marcel Aloy

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Gilles de Truchis

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

Estimation of bivariate fractionally cointegrated models usually operates in two steps: the first step is to estimate the long run coefficient (\beta) whereas the second step estimates the long memory parameter (d) of the cointegrating residuals. We suggest an adaptation of the maximum likelihood estimator of Hualde and Robinson (2007) to estimate jointly \beta and d, and possibly other nuisance parameters, for a wide range of integration orders when regressors are I(1). The finite sample properties of this estimator are compared with various popular estimation methods of parameters \beta (LSE, ADL, DOLS, FMLS, GLS, MLE, NBLS, FMNBLS), and d (LPE,LWE,LPM,FML) through a Monte Carlo experiment. We also investigate the crucial question of testing for fractional cointegration (that is, d

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  • Marcel Aloy & Gilles de Truchis, 2012. "Estimation and Testing for Fractional Cointegration," Working Papers halshs-00793206, HAL.
    • Handle: RePEc:hal:wpaper:halshs-00793206
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    2. Canarella, Giorgio & Miller, Stephen M., 2017. "Inflation targeting and inflation persistence: New evidence from fractional integration and cointegration," Journal of Economics and Business, Elsevier, vol. 92(C), pages 45-62.

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    More about this item

    Keywords

    Fractional cointegration; Long memory; Monte Carlo experiment; Cointegration test;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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