Semiparametric Sieve-Type GLS Inference in Regressions with Long-Range Dependence
AbstractThis paper considers the problem of statistical inference in linear regression models whose stochastic regressors and errors may exhibit long-range dependence. A time-domain sieve-type generalized least squares (GLS) procedure is proposed based on an autoregressive approximation to the generating mechanism of the errors. The asymptotic properties of the sieve-type GLS estimator are established. A Monte Carlo study examines the finite-sample properties of the method for testing regression hypotheses.
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 Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 587.
Date of creation: Mar 2007
Date of revision:
Autoregressive approximation; Generalized least squares; Linear regression; Long-range dependence; Spectral density;
Find related papers by 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 &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-03-10 (All new papers)
- NEP-ECM-2007-03-10 (Econometrics)
- NEP-ETS-2007-03-10 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Nicholas M. Kiefer & Timothy J. Vogelsang & Helle Bunzel, 2000.
"Simple Robust Testing of Regression Hypotheses,"
Econometric Society, vol. 68(3), pages 695-714, May.
- Yongmiao Hong & Halbert White, 2005. "Asymptotic Distribution Theory for Nonparametric Entropy Measures of Serial Dependence," Econometrica, Econometric Society, vol. 73(3), pages 837-901, 05.
- Nielsen, Morten Oe., .
"Semiparametric Estimation in Time Series Regression with Long Range Dependence,"
Economics Working Papers
2002-17, School of Economics and Management, University of Aarhus.
- Morten Orregaard Nielsen, 2005. "Semiparametric Estimation in Time-Series Regression with Long-Range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 279-304, 03.
- D. S. Poskitt, 2005. "Autoregressive Approximation in Nonstandard Situations: The Non-Invertible and Fractionally Integrated Cases," Monash Econometrics and Business Statistics Working Papers 16/05, Monash University, Department of Econometrics and Business Statistics.
- Andrews, Donald W K & Monahan, J Christopher, 1992.
"An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator,"
Econometric Society, vol. 60(4), pages 953-66, July.
- Donald W.K. Andrews & Christopher J. Monahan, 1990. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Cowles Foundation Discussion Papers 942, Cowles Foundation for Research in Economics, Yale University.
- Andrews, Donald W K, 1991.
"Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation,"
Econometric Society, vol. 59(3), pages 817-58, May.
- Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
- Javier Hidalgo & Peter M. Robinson, 2002.
"Adapting to Unknown Disturbance Autocorrelation in Regression with Long Memory,"
Econometric Society, vol. 70(4), pages 1545-1581, July.
- Javier Hidalgo & Peter M Robinson, 2001. "Adapting to Unknown Disturbance Autocorrelation in Regression with Long Memory," STICERD - Econometrics Paper Series /2001/427, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Amemiya, Takeshi, 1973. "Generalized Least Squares with an Estimated Autocovariance Matrix," Econometrica, Econometric Society, vol. 41(4), pages 723-32, July.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nick Vriend).
If references are entirely missing, you can add them using this form.