Fractional integration with Drift: Estimation in Small Samples
AbstractWe examine the finite-sample behavior of estimators of the order of integration in a fractionally integrated time-series model. In particular, we compare exact time-domain likelihood estimation to frequency-domain approximate likelihood estimation. We show that over-differencing is of critical importance for time-domain maximum-likelihood estimation in finite samples. Over-differencing moves the differencing parameter (in the over-differenced model) away from the boundary of the parameter space, while at the same time obviating the need to estimate the drift parameter. The two estimators that we compare are asymptotically equivalent. In small samples, however, the time-domain estimator has smaller mean squared error than the frequency-domain estimator. Although the frequency-domain estimator has larger bias than the time-domain estimator for some regions of the parameter bias, it can also have smaller bias. We use a simulation procedure which exploits the approximate linearity of the bias function to reduce the bias in the time-domain estimator.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Carnegie Mellon University, Tepper School of Business in its series GSIA Working Papers with number 22.
Date of creation:
Date of revision:
Contact details of provider:
Postal: Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890
Web page: http://www.tepper.cmu.edu/
Other versions of this item:
- Smith, Anthony A, Jr & Sowell, Fallaw & Zin, Stanley E, 1997. "Fractional Integration with Drift: Estimation in Small Samples," Empirical Economics, Springer, vol. 22(1), pages 103-16.
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Jurgen A. Doornik & Marius Ooms, 2001.
"Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models,"
2001-W27, Economics Group, Nuffield College, University of Oxford.
- Doornik, Jurgen A. & Ooms, Marius, 2003. "Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 333-348, March.
- Jurgen Doornik & Marius Ooms, 2001. "Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models," Economics Series Working Papers 2001-W27, University of Oxford, Department of Economics.
- Emma Iglesias & Garry Phillips, 2005. "Analysing one-month Euro-market interest rates by fractionally integrated models," Applied Financial Economics, Taylor & Francis Journals, vol. 15(2), pages 95-106.
- Evans, Mark, 2011. "Steel consumption and economic activity in the UK: The integration and cointegration debate," Resources Policy, Elsevier, vol. 36(2), pages 97-106, June.
- Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2005.
"Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study,"
Trinity Economics Papers
tep20021, Trinity College Dublin, Department of Economics.
- Bond, Derek & Harrison, Michael J & O’Brien, Edward J., 2006. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Research Technical Papers 2/RT/06, Central Bank of Ireland.
- Bond, Derek & Harrison, Michael J & Hession, Niall & O’Brien, Edward J., 2006.
"Some Empirical Observations on the Forward Exchange Rate Anomaly,"
Research Technical Papers
3/RT/06, Central Bank of Ireland.
- Derek Bond & Michael J. Harrison & Niall Hession & Edward J. O'Brien, 2006. "Some Empirical Observations on the Forward Exchange Rate Anomaly," Trinity Economics Papers tep2006, Trinity College Dublin, Department of Economics.
- James G. MacKinnon & Anthony A. Smith Jr., 1995.
"Approximate Bias Correction in Econometrics,"
919, Queen's University, Department of Economics.
- Mackinnon, J.G. & Smith, A.A., 1996. "Approximate Bias Correction in Econometrics," G.R.E.Q.A.M. 96a14, Universite Aix-Marseille III.
- James G. MacKinnon & Anthony A. Smith, Jr., . "Approximate Bias Correction in Econometrics," GSIA Working Papers 1997-36, Carnegie Mellon University, Tepper School of Business.
- Carlos Pestana Barros & Luis Gil-Alana, 2006. "Eta: A Persistent Phenomenon," Defence and Peace Economics, Taylor & Francis Journals, vol. 17(2), pages 95-116.
- Ana Pérez & Esther Ruiz, 2002.
"Modelos de memoria larga para series económicas y financieras,"
Fundación SEPI, vol. 26(3), pages 395-445, September.
- Ana Pérez & Esther Ruiz, 2001. "Modelos De Memoria Larga Para Series Económicas Y Financieras," Documentos de Trabajo de EstadÃstica y EconometrÃa ds010101, Universidad Carlos III, Departamento de Estadística y Econometría.
- Cunado, J. & Gil-Alana, L. A. & Perez de Gracia, F., 2004. "Real convergence in Taiwan: a fractionally integrated approach," Journal of Asian Economics, Elsevier, vol. 15(3), pages 529-547, June.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Steve Spear).
If references are entirely missing, you can add them using this form.