Fully modified semiparametric GLS estimation for regressions with nonstationary seasonal regressors

Dong Wan Shin; Man Suk Oh

doi:10.1016/j.jeconom.2003.07.001

Fully modified semiparametric GLS estimation for regressions with nonstationary seasonal regressors

Dong Wan Shin, Man Suk Oh

Department of Statistics

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Regression models with seasonally integrated and possibly endogenous regressors and serially correlated regression errors are studied. Spectral decompositions of generalized sums of cross products of regressors and regression errors are used to develop a feasible generalized least squares estimator (FGLSE) which does not require parametric specifications for error processes. Using the FGLSE and following the spirit of "Fully Modified estimation" of Phillips and Hansen (Rev. Econ. Stud. 57 (1990) 99), a fully modified GLSE (FM-GLSE) and inference procedures are constructed. The distribution of the FM-GLSE is shown to be asymptotically a mixed normal distribution which validates standard inference based on the FM-GLSE with normal theory. A Monte-Carlo simulation shows that the FM-GLSE is more efficient than the ordinary least squares estimator (OLSE) in the cases of endogeneity or serial correlation and more efficient than the FM-estimator based on the OLSE in the case of serial correlation.

Original language	English
Pages (from-to)	247-280
Number of pages	34
Journal	Journal of Econometrics
Volume	122
Issue number	2
DOIs	https://doi.org/10.1016/j.jeconom.2003.07.001
State	Published - Oct 2004

Bibliographical note

Funding Information:
The authors are very grateful to the three anonymous referees, an associate editor, and Professor B.U. Park for their helpful comments which lead to significant improvement of the paper. This work was supported by Korea Research Foundation grant KRF- 2002-070-C0001.

Keywords

Efficiency
Fourier coefficients
Normality
Semiparametric estimation
Spectral decomposition

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10.1016/j.jeconom.2003.07.001

Cite this

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title = "Fully modified semiparametric GLS estimation for regressions with nonstationary seasonal regressors",

abstract = "Regression models with seasonally integrated and possibly endogenous regressors and serially correlated regression errors are studied. Spectral decompositions of generalized sums of cross products of regressors and regression errors are used to develop a feasible generalized least squares estimator (FGLSE) which does not require parametric specifications for error processes. Using the FGLSE and following the spirit of {"}Fully Modified estimation{"} of Phillips and Hansen (Rev. Econ. Stud. 57 (1990) 99), a fully modified GLSE (FM-GLSE) and inference procedures are constructed. The distribution of the FM-GLSE is shown to be asymptotically a mixed normal distribution which validates standard inference based on the FM-GLSE with normal theory. A Monte-Carlo simulation shows that the FM-GLSE is more efficient than the ordinary least squares estimator (OLSE) in the cases of endogeneity or serial correlation and more efficient than the FM-estimator based on the OLSE in the case of serial correlation.",

keywords = "Efficiency, Fourier coefficients, Normality, Semiparametric estimation, Spectral decomposition",

author = "Shin, {Dong Wan} and Oh, {Man Suk}",

note = "Funding Information: The authors are very grateful to the three anonymous referees, an associate editor, and Professor B.U. Park for their helpful comments which lead to significant improvement of the paper. This work was supported by Korea Research Foundation grant KRF- 2002-070-C0001. ",

year = "2004",

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AU - Oh, Man Suk

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PY - 2004/10

Y1 - 2004/10

N2 - Regression models with seasonally integrated and possibly endogenous regressors and serially correlated regression errors are studied. Spectral decompositions of generalized sums of cross products of regressors and regression errors are used to develop a feasible generalized least squares estimator (FGLSE) which does not require parametric specifications for error processes. Using the FGLSE and following the spirit of "Fully Modified estimation" of Phillips and Hansen (Rev. Econ. Stud. 57 (1990) 99), a fully modified GLSE (FM-GLSE) and inference procedures are constructed. The distribution of the FM-GLSE is shown to be asymptotically a mixed normal distribution which validates standard inference based on the FM-GLSE with normal theory. A Monte-Carlo simulation shows that the FM-GLSE is more efficient than the ordinary least squares estimator (OLSE) in the cases of endogeneity or serial correlation and more efficient than the FM-estimator based on the OLSE in the case of serial correlation.

AB - Regression models with seasonally integrated and possibly endogenous regressors and serially correlated regression errors are studied. Spectral decompositions of generalized sums of cross products of regressors and regression errors are used to develop a feasible generalized least squares estimator (FGLSE) which does not require parametric specifications for error processes. Using the FGLSE and following the spirit of "Fully Modified estimation" of Phillips and Hansen (Rev. Econ. Stud. 57 (1990) 99), a fully modified GLSE (FM-GLSE) and inference procedures are constructed. The distribution of the FM-GLSE is shown to be asymptotically a mixed normal distribution which validates standard inference based on the FM-GLSE with normal theory. A Monte-Carlo simulation shows that the FM-GLSE is more efficient than the ordinary least squares estimator (OLSE) in the cases of endogeneity or serial correlation and more efficient than the FM-estimator based on the OLSE in the case of serial correlation.

KW - Efficiency

KW - Fourier coefficients

KW - Normality

KW - Semiparametric estimation

KW - Spectral decomposition

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ER -

Fully modified semiparametric GLS estimation for regressions with nonstationary seasonal regressors

Abstract

Bibliographical note

Keywords

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